Overview

Dataset statistics

Number of variables	39
Number of observations	10831
Missing cells	162201
Missing cells (%)	38.4%
Duplicate rows	0
Duplicate rows (%)	0.0%
Total size in memory	77.7 MiB
Average record size in memory	7.3 KiB

Variable types

Text	22
Numeric	7
Categorical	5
Boolean	4
Unsupported	1

Alerts

`source_scopus` has constant value "True"	Constant
`source_wos` has constant value "True"	Constant
`cited_by` is highly overall correlated with `cited_by_scopus` and 2 other fields	High correlation
`cited_by_scopus` is highly overall correlated with `cited_by` and 3 other fields	High correlation
`cited_reference_count_wos` is highly overall correlated with `has_wos`	High correlation
`document_type` is highly overall correlated with `document_type_wos` and 2 other fields	High correlation
`document_type_wos` is highly overall correlated with `document_type` and 2 other fields	High correlation
`has_scopus` is highly overall correlated with `cited_by_scopus` and 2 other fields	High correlation
`has_wos` is highly overall correlated with `cited_reference_count_wos` and 6 other fields	High correlation
`language_scopus` is highly overall correlated with `has_scopus` and 1 other fields	High correlation
`language_wos` is highly overall correlated with `has_wos` and 1 other fields	High correlation
`publication_type_wos` is highly overall correlated with `document_type` and 2 other fields	High correlation
`times_cited_wos_all` is highly overall correlated with `cited_by` and 3 other fields	High correlation
`times_cited_wos_core` is highly overall correlated with `cited_by` and 3 other fields	High correlation
`year` is highly overall correlated with `year_wos`	High correlation
`year_wos` is highly overall correlated with `has_wos` and 1 other fields	High correlation
`document_type` is highly imbalanced (58.5%)	Imbalance
`has_scopus` is highly imbalanced (64.6%)	Imbalance
`language_scopus` is highly imbalanced (93.3%)	Imbalance
`document_type_wos` is highly imbalanced (61.7%)	Imbalance
`language_wos` is highly imbalanced (92.9%)	Imbalance
`journal` has 2020 (18.7%) missing values	Missing
`authors` has 754 (7.0%) missing values	Missing
`author_keywords` has 2589 (23.9%) missing values	Missing
`index_keywords` has 4570 (42.2%) missing values	Missing
`abstract` has 724 (6.7%) missing values	Missing
`cited_by_scopus` has 724 (6.7%) missing values	Missing
`references_count_scopus` has 1001 (9.2%) missing values	Missing
`publisher_scopus` has 1611 (14.9%) missing values	Missing
`language_scopus` has 724 (6.7%) missing values	Missing
`affiliations_scopus` has 872 (8.1%) missing values	Missing
`country_scopus` has 10831 (100.0%) missing values	Missing
`source_scopus` has 724 (6.7%) missing values	Missing
`title_wos` has 6639 (61.3%) missing values	Missing
`year_wos` has 6639 (61.3%) missing values	Missing
`journal_wos` has 6639 (61.3%) missing values	Missing
`document_type_wos` has 6639 (61.3%) missing values	Missing
`publication_type_wos` has 6639 (61.3%) missing values	Missing
`authors_wos` has 6640 (61.3%) missing values	Missing
`author_full_names_wos` has 6640 (61.3%) missing values	Missing
`author_keywords_wos` has 6935 (64.0%) missing values	Missing
`keywords_plus_wos` has 8288 (76.5%) missing values	Missing
`abstract_wos` has 6720 (62.0%) missing values	Missing
`times_cited_wos_core` has 6639 (61.3%) missing values	Missing
`times_cited_wos_all` has 6639 (61.3%) missing values	Missing
`cited_reference_count_wos` has 6639 (61.3%) missing values	Missing
`wos_categories` has 6654 (61.4%) missing values	Missing
`research_areas` has 6654 (61.4%) missing values	Missing
`publisher_wos` has 6639 (61.3%) missing values	Missing
`language_wos` has 6639 (61.3%) missing values	Missing
`affiliations_wos` has 6848 (63.2%) missing values	Missing
`addresses_wos` has 6648 (61.4%) missing values	Missing
`source_wos` has 6639 (61.3%) missing values	Missing
`cited_by` is highly skewed (γ1 = 47.31713708)	Skewed
`cited_by_scopus` is highly skewed (γ1 = 45.89522471)	Skewed
`times_cited_wos_core` is highly skewed (γ1 = 36.20691352)	Skewed
`times_cited_wos_all` is highly skewed (γ1 = 36.78078926)	Skewed
`country_scopus` is an unsupported type, check if it needs cleaning or further analysis	Unsupported
`cited_by` has 2480 (22.9%) zeros	Zeros
`cited_by_scopus` has 2301 (21.2%) zeros	Zeros
`times_cited_wos_core` has 1047 (9.7%) zeros	Zeros
`times_cited_wos_all` has 903 (8.3%) zeros	Zeros

Reproduction

Analysis started	2026-01-14 04:55:37.066506
Analysis finished	2026-01-14 04:56:03.477134
Duration	26.41 seconds
Software version	ydata-profiling vv4.18.1
Download configuration	config.json

doi
Text

Distinct	10830
Distinct (%)	100.0%
Missing	1
Missing (%)	< 0.1%
Memory size	787.8 KiB

Length

Max length	66
Median length	58
Mean length	25.476547
Min length	12

Characters and Unicode

Total characters	275911
Distinct characters	48
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	10830 ?
Unique (%)	100.0%

Sample

1st row	10.1002/(sici)1096-9128(199601)8:1<47::aid-cpe194>3.0.co;2-9
2nd row	10.1002/(sici)1097-0193(1999)8:2/3<128::aid-hbm10>3.0.co;2-g
3rd row	10.1002/(sici)1097-0363(199706)24:12<1321::aid-fld562>3.0.co;2-l
4th row	10.1002/(sici)1097-0363(199706)24:12<1353::aid-fld564>3.0.co;2-6
5th row	10.1002/(sici)1097-0363(199706)24:12<1371::aid-fld565>3.0.co;2-7

Value	Count	Frequency (%)
10.1002/9780470670606.wbecc0026	1	< 0.1%
10.1002/(sici)1096-9128(199601)8:1<47::aid-cpe194>3.0.co;2-9	1	< 0.1%
10.1002/(sici)1097-0193(1999)8:2/3<128::aid-hbm10>3.0.co;2-g	1	< 0.1%
10.1002/(sici)1097-0363(199706)24:12<1321::aid-fld562>3.0.co;2-l	1	< 0.1%
10.1002/(sici)1097-0363(199706)24:12<1353::aid-fld564>3.0.co;2-6	1	< 0.1%
10.1002/(sici)1097-0363(199706)24:12<1371::aid-fld565>3.0.co;2-7	1	< 0.1%
10.1002/(sici)1097-0363(199706)24:12<1417::aid-fld567>3.0.co;2-n	1	< 0.1%
10.1002/(sici)1097-0363(199706)24:12<1449::aid-fld569>3.0.co;2-8	1	< 0.1%
10.1002/(sici)1098-111x(199702)12:2<105::aid-int1>3.0.co;2-u	1	< 0.1%
10.1002/(sici)1098-111x(199911)14:11<1071::aid-int1>3.0.co;2-j	1	< 0.1%
Other values (10820)	10820	99.9%

Most occurring characters

Value	Count	Frequency (%)
1	43930	15.9%
0	42913	15.6%
.	24004	8.7%
2	20402	7.4%
3	16725	6.1%
9	14171	5.1%
7	12822	4.6%
4	12372	4.5%
5	12348	4.5%
8	11762	4.3%
Other values (38)	64462	23.4%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	275911	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
1	43930	15.9%
0	42913	15.6%
.	24004	8.7%
2	20402	7.4%
3	16725	6.1%
9	14171	5.1%
7	12822	4.6%
4	12372	4.5%
5	12348	4.5%
8	11762	4.3%
Other values (38)	64462	23.4%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	275911	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
1	43930	15.9%
0	42913	15.6%
.	24004	8.7%
2	20402	7.4%
3	16725	6.1%
9	14171	5.1%
7	12822	4.6%
4	12372	4.5%
5	12348	4.5%
8	11762	4.3%
Other values (38)	64462	23.4%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	275911	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
1	43930	15.9%
0	42913	15.6%
.	24004	8.7%
2	20402	7.4%
3	16725	6.1%
9	14171	5.1%
7	12822	4.6%
4	12372	4.5%
5	12348	4.5%
8	11762	4.3%
Other values (38)	64462	23.4%

title
Text

Distinct	10776
Distinct (%)	99.5%
Missing	0
Missing (%)	0.0%
Memory size	1.7 MiB

Length

Max length	476
Median length	259
Mean length	93.42009
Min length	6

Characters and Unicode

Total characters	1011833
Distinct characters	700
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	10731 ?
Unique (%)	99.1%

Sample

1st row	Benchmarking the computation and communication performance of the CM-5
2nd row	Computational modeling of high-level cognition and brain function
3rd row	Parallel computation of incompressible flows with complex geometries
4th row	Parallel finite element simulation of large ram-air parachutes
5th row	Parallel finite element methods for large-scale computation of storm surges and tidal flows

Value	Count	Frequency (%)
of	5376	4.1%
and	4854	3.7%
in	4486	3.4%
computational	4310	3.3%
thinking	4264	3.3%
the	3745	2.9%
a	3593	2.8%
for	2646	2.0%
to	1967	1.5%
learning	1743	1.3%
Other values (13021)	93466	71.6%

Most occurring characters

Value	Count	Frequency (%)
	119386	11.8%
i	80198	7.9%
n	79272	7.8%
e	76564	7.6%
t	67002	6.6%
a	66775	6.6%
o	66645	6.6%
r	46219	4.6%
s	41410	4.1%
l	34944	3.5%
Other values (690)	333418	33.0%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	1011833	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	119386	11.8%
i	80198	7.9%
n	79272	7.8%
e	76564	7.6%
t	67002	6.6%
a	66775	6.6%
o	66645	6.6%
r	46219	4.6%
s	41410	4.1%
l	34944	3.5%
Other values (690)	333418	33.0%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	1011833	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	119386	11.8%
i	80198	7.9%
n	79272	7.8%
e	76564	7.6%
t	67002	6.6%
a	66775	6.6%
o	66645	6.6%
r	46219	4.6%
s	41410	4.1%
l	34944	3.5%
Other values (690)	333418	33.0%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	1011833	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	119386	11.8%
i	80198	7.9%
n	79272	7.8%
e	76564	7.6%
t	67002	6.6%
a	66775	6.6%
o	66645	6.6%
r	46219	4.6%
s	41410	4.1%
l	34944	3.5%
Other values (690)	333418	33.0%

year
Real number (ℝ)

High correlation

Distinct	48
Distinct (%)	0.4%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	2019.465

Minimum	1970
Maximum	2026
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	95.3 KiB

Quantile statistics

Minimum	1970
5-th percentile	2008
Q1	2018
median	2021
Q3	2024
95-th percentile	2025
Maximum	2026
Range	56
Interquartile range (IQR)	6

Descriptive statistics

Standard deviation	6.025465
Coefficient of variation (CV)	0.0029836938
Kurtosis	6.6109073
Mean	2019.465
Median Absolute Deviation (MAD)	3
Skewness	-2.1895759
Sum	21872825
Variance	36.306228
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=48)

Value	Count	Frequency (%)
2024	1377	12.7%
2025	1366	12.6%
2023	1188	11.0%
2022	1070	9.9%
2021	982	9.1%
2020	852	7.9%
2019	723	6.7%
2018	562	5.2%
2017	467	4.3%
2016	302	2.8%
Other values (38)	1942	17.9%

Value	Count	Frequency (%)
1970	1	< 0.1%
1975	1	< 0.1%
1980	1	< 0.1%
1981	1	< 0.1%
1982	1	< 0.1%
1983	4	< 0.1%
1984	6	0.1%
1986	3	< 0.1%
1987	3	< 0.1%
1988	7	0.1%

Value	Count	Frequency (%)
2026	64	0.6%
2025	1366	12.6%
2024	1377	12.7%
2023	1188	11.0%
2022	1070	9.9%
2021	982	9.1%
2020	852	7.9%
2019	723	6.7%
2018	562	5.2%
2017	467	4.3%

journal
Text

Missing

Distinct	2639
Distinct (%)	30.0%
Missing	2020
Missing (%)	18.7%
Memory size	856.7 KiB

Length

Max length	242
Median length	115
Mean length	43.213256
Min length	3

Characters and Unicode

Total characters	380752
Distinct characters	77
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	1666 ?
Unique (%)	18.9%

Sample

1st row	Concurrency Practice and Experience
2nd row	Human Brain Mapping
3rd row	International Journal for Numerical Methods in Fluids
4th row	International Journal for Numerical Methods in Fluids
5th row	International Journal for Numerical Methods in Fluids

Value	Count	Frequency (%)
of	2732	5.6%
and	2708	5.6%
education	2457	5.1%
in	2253	4.6%
journal	1784	3.7%
conference	1673	3.4%
science	1520	3.1%
international	1488	3.1%
on	1374	2.8%
computer	1253	2.6%
Other values (2267)	29387	60.4%

Most occurring characters

Value	Count	Frequency (%)
	39818	10.5%
n	29564	7.8%
e	26567	7.0%
o	21919	5.8%
i	20804	5.5%
a	17775	4.7%
t	15316	4.0%
c	14553	3.8%
E	14486	3.8%
r	14419	3.8%
Other values (67)	165531	43.5%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	380752	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	39818	10.5%
n	29564	7.8%
e	26567	7.0%
o	21919	5.8%
i	20804	5.5%
a	17775	4.7%
t	15316	4.0%
c	14553	3.8%
E	14486	3.8%
r	14419	3.8%
Other values (67)	165531	43.5%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	380752	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	39818	10.5%
n	29564	7.8%
e	26567	7.0%
o	21919	5.8%
i	20804	5.5%
a	17775	4.7%
t	15316	4.0%
c	14553	3.8%
E	14486	3.8%
r	14419	3.8%
Other values (67)	165531	43.5%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	380752	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	39818	10.5%
n	29564	7.8%
e	26567	7.0%
o	21919	5.8%
i	20804	5.5%
a	17775	4.7%
t	15316	4.0%
c	14553	3.8%
E	14486	3.8%
r	14419	3.8%
Other values (67)	165531	43.5%

document_type
Categorical

High correlation Imbalance

Distinct	25
Distinct (%)	0.2%
Missing	0
Missing (%)	0.0%
Memory size	634.1 KiB

Article	4874
Conference paper	4073
Book chapter	792
Review	468
Book	188
Other values (20)	436

Length

Max length	32
Median length	26
Mean length	10.938233
Min length	4

Characters and Unicode

Total characters	118472
Distinct characters	35
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	5 ?
Unique (%)	< 0.1%

Sample

1st row	Article
2nd row	Conference paper
3rd row	Article
4th row	Article
5th row	Article

Common Values

Value	Count	Frequency (%)
Article	4874	45.0%
Conference paper	4073	37.6%
Book chapter	792	7.3%
Review	468	4.3%
Book	188	1.7%
Proceedings Paper	174	1.6%
Editorial	49	0.5%
Note	46	0.4%
Article; Book Chapter	46	0.4%
Erratum	28	0.3%
Other values (15)	93	0.9%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
article	4940	30.8%
paper	4251	26.5%
conference	4073	25.4%
book	1032	6.4%
chapter	840	5.2%
review	477	3.0%
proceedings	176	1.1%
editorial	60	0.4%
note	46	0.3%
erratum	28	0.2%
Other values (12)	139	0.9%

Most occurring characters

Value	Count	Frequency (%)
e	23719	20.0%
r	14499	12.2%
c	10046	8.5%
p	9167	7.7%
n	8335	7.0%
o	6438	5.4%
t	6023	5.1%
i	5737	4.8%
a	5249	4.4%
	5231	4.4%
Other values (25)	24028	20.3%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	118472	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
e	23719	20.0%
r	14499	12.2%
c	10046	8.5%
p	9167	7.7%
n	8335	7.0%
o	6438	5.4%
t	6023	5.1%
i	5737	4.8%
a	5249	4.4%
	5231	4.4%
Other values (25)	24028	20.3%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	118472	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
e	23719	20.0%
r	14499	12.2%
c	10046	8.5%
p	9167	7.7%
n	8335	7.0%
o	6438	5.4%
t	6023	5.1%
i	5737	4.8%
a	5249	4.4%
	5231	4.4%
Other values (25)	24028	20.3%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	118472	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
e	23719	20.0%
r	14499	12.2%
c	10046	8.5%
p	9167	7.7%
n	8335	7.0%
o	6438	5.4%
t	6023	5.1%
i	5737	4.8%
a	5249	4.4%
	5231	4.4%
Other values (25)	24028	20.3%

cited_by
Real number (ℝ)

High correlation Skewed Zeros

Distinct	308
Distinct (%)	2.8%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	20.698366

Minimum	0
Maximum	10043
Zeros	2480
Zeros (%)	22.9%
Negative	0
Negative (%)	0.0%
Memory size	84.7 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	1
median	4
Q3	14
95-th percentile	75
Maximum	10043
Range	10043
Interquartile range (IQR)	13

Descriptive statistics

Standard deviation	135.71259
Coefficient of variation (CV)	6.5566814
Kurtosis	3062.8841
Mean	20.698366
Median Absolute Deviation (MAD)	4
Skewness	47.317137
Sum	224184
Variance	18417.907
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0	2480	22.9%
1	1315	12.1%
2	834	7.7%
3	659	6.1%
4	534	4.9%
5	411	3.8%
6	333	3.1%
7	293	2.7%
8	264	2.4%
9	243	2.2%
Other values (298)	3465	32.0%

Value	Count	Frequency (%)
0	2480	22.9%
1	1315	12.1%
2	834	7.7%
3	659	6.1%
4	534	4.9%
5	411	3.8%
6	333	3.1%
7	293	2.7%
8	264	2.4%
9	243	2.2%

Value	Count	Frequency (%)
10043	1	< 0.1%
5509	1	< 0.1%
3161	1	< 0.1%
3156	1	< 0.1%
2123	1	< 0.1%
1908	1	< 0.1%
1703	1	< 0.1%
1438	1	< 0.1%
1317	1	< 0.1%
1306	1	< 0.1%

has_scopus
Boolean

High correlation Imbalance

Distinct	2
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	10.7 KiB

True	10107
False	724

Value	Count	Frequency (%)
True	10107	93.3%
False	724	6.7%

has_wos
Boolean

High correlation

Distinct	2
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	10.7 KiB

False	6639
True	4192

Value	Count	Frequency (%)
False	6639	61.3%
True	4192	38.7%

authors
Text

Missing

Distinct	9360
Distinct (%)	92.9%
Missing	754
Missing (%)	7.0%
Memory size	1.0 MiB

Length

Max length	620
Median length	229
Mean length	41.763124
Min length	6

Characters and Unicode

Total characters	420847
Distinct characters	147
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	8874 ?
Unique (%)	88.1%

Sample

1st row	Dinçer, K.; Bozkus, Z.; Ranka, S.; Fox, G.
2nd row	Just, M.A.; Carpenter, P.A.; Varma, S.
3rd row	Johnson, A.A.; Tezduyar, T.
4th row	Kalro, V.; Aliabadi, S.; Garrard, W.; Tezduyar, T.; Mittal, S.; Stein, K.
5th row	Kashiyama, K.; Saitoh, K.; Behr, M.; Tezduyar, T.

Value	Count	Frequency (%)
m	2273	3.4%
a	2055	3.1%
j	1920	2.9%
s	1881	2.8%
c	1277	1.9%
d	1127	1.7%
r	1090	1.6%
y	1024	1.5%
l	1019	1.5%
k	899	1.3%
Other values (16181)	52743	78.4%

Most occurring characters

Value	Count	Frequency (%)
	57229	13.6%
.	43198	10.3%
,	32894	7.8%
a	25990	6.2%
;	22884	5.4%
e	17892	4.3%
n	16896	4.0%
i	15251	3.6%
r	13961	3.3%
o	13901	3.3%
Other values (137)	160751	38.2%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	420847	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	57229	13.6%
.	43198	10.3%
,	32894	7.8%
a	25990	6.2%
;	22884	5.4%
e	17892	4.3%
n	16896	4.0%
i	15251	3.6%
r	13961	3.3%
o	13901	3.3%
Other values (137)	160751	38.2%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	420847	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	57229	13.6%
.	43198	10.3%
,	32894	7.8%
a	25990	6.2%
;	22884	5.4%
e	17892	4.3%
n	16896	4.0%
i	15251	3.6%
r	13961	3.3%
o	13901	3.3%
Other values (137)	160751	38.2%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	420847	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	57229	13.6%
.	43198	10.3%
,	32894	7.8%
a	25990	6.2%
;	22884	5.4%
e	17892	4.3%
n	16896	4.0%
i	15251	3.6%
r	13961	3.3%
o	13901	3.3%
Other values (137)	160751	38.2%

author_keywords
Text

Missing

Distinct	8181
Distinct (%)	99.3%
Missing	2589
Missing (%)	23.9%
Memory size	1.3 MiB

Length

Max length	930
Median length	274
Mean length	98.32577
Min length	6

Characters and Unicode

Total characters	810401
Distinct characters	135
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	8123 ?
Unique (%)	98.6%

Sample

1st row	4CAPS; Brain function; PET; T-MRI
2nd row	Automobile; Complex geometries; Mesh generation; Parallel flow simulation
3rd row	3D flow simulations; Parachutes; Parallel computations
4th row	Implicit space-time formulation; Parallel finite element method; Storm surge; Three-step explicit formulation; Tidal flow
5th row	Compressible flows; Missile aerodynamics; Parallel computing methods

Value	Count	Frequency (%)
computational	5251	6.3%
thinking	5175	6.2%
education	2817	3.4%
learning	2307	2.8%
programming	1539	1.8%
science	1082	1.3%
design	973	1.2%
computer	850	1.0%
and	717	0.9%
of	602	0.7%
Other values (9148)	61942	74.4%

Most occurring characters

Value	Count	Frequency (%)
	75003	9.3%
i	70907	8.7%
n	63449	7.8%
e	59468	7.3%
t	57522	7.1%
a	55765	6.9%
o	50958	6.3%
r	35586	4.4%
;	33956	4.2%
l	32439	4.0%
Other values (125)	275348	34.0%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	810401	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	75003	9.3%
i	70907	8.7%
n	63449	7.8%
e	59468	7.3%
t	57522	7.1%
a	55765	6.9%
o	50958	6.3%
r	35586	4.4%
;	33956	4.2%
l	32439	4.0%
Other values (125)	275348	34.0%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	810401	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	75003	9.3%
i	70907	8.7%
n	63449	7.8%
e	59468	7.3%
t	57522	7.1%
a	55765	6.9%
o	50958	6.3%
r	35586	4.4%
;	33956	4.2%
l	32439	4.0%
Other values (125)	275348	34.0%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	810401	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	75003	9.3%
i	70907	8.7%
n	63449	7.8%
e	59468	7.3%
t	57522	7.1%
a	55765	6.9%
o	50958	6.3%
r	35586	4.4%
;	33956	4.2%
l	32439	4.0%
Other values (125)	275348	34.0%

index_keywords
Text

Missing

Distinct	6257
Distinct (%)	99.9%
Missing	4570
Missing (%)	42.2%
Memory size	2.1 MiB

Length

Max length	2655
Median length	690
Mean length	280.32982
Min length	7

Characters and Unicode

Total characters	1755145
Distinct characters	115
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	6253 ?
Unique (%)	99.9%

Sample

1st row	Bandwidth; Calculations; Computational methods; Computer networks; Data communication systems; Distributed computer systems; Mathematical models; Parallel algorithms; Performance; Standards; Synchronization; Topology; Benchmarking; Communication latency; Communication start-up time; Computational processing rate; Control network; Diagnostic network; Gaussian elimination code; Global communication; Point to point communication; Vectorization; Parallel processing systems
2nd row	brain function; cognition; computer model; computer simulation; conference paper; image processing; imaging system; nuclear magnetic resonance imaging; priority journal; Brain; Cognition; Humans; Magnetic Resonance Imaging; Models, Neurological; Neural Networks (Computer); Thinking
3rd row	Aerodynamics; Automobiles; Computer simulation; Finite element method; Flow interactions; Navier Stokes equations; Parallel processing systems; Incompressible flow; Computational fluid dynamics; air flow; incompressible flow; Navier-Stokes equations; vehicles
4th row	Computational fluid dynamics; Computer simulation; Drag; Finite element method; Lift; Mathematical models; Navier Stokes equations; Newtonian flow; Parallel processing systems; Three dimensional computer graphics; Canopy inflation; Ram air parachutes; Parachutes; computer simulation; finite element method; parachutes
5th row	Computational fluid dynamics; Computer simulation; Finite element method; Parallel processing systems; Tides; Tidal flows; Unstructured grid formulations; Storms; computer simulation; finite element method; storms; tidal flows

Value	Count	Frequency (%)
computational	5667	3.1%
education	4287	2.4%
learning	4034	2.2%
computer	3300	1.8%
thinkings	3120	1.7%
programming	2822	1.6%
students	2640	1.5%
systems	2371	1.3%
engineering	1985	1.1%
computing	1970	1.1%
Other values (11047)	148514	82.2%

Most occurring characters

Value	Count	Frequency (%)
	174449	9.9%
e	139615	8.0%
i	137124	7.8%
n	129507	7.4%
t	117560	6.7%
a	113948	6.5%
o	107694	6.1%
;	89057	5.1%
s	85286	4.9%
r	84748	4.8%
Other values (105)	576157	32.8%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	1755145	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	174449	9.9%
e	139615	8.0%
i	137124	7.8%
n	129507	7.4%
t	117560	6.7%
a	113948	6.5%
o	107694	6.1%
;	89057	5.1%
s	85286	4.9%
r	84748	4.8%
Other values (105)	576157	32.8%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	1755145	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	174449	9.9%
e	139615	8.0%
i	137124	7.8%
n	129507	7.4%
t	117560	6.7%
a	113948	6.5%
o	107694	6.1%
;	89057	5.1%
s	85286	4.9%
r	84748	4.8%
Other values (105)	576157	32.8%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	1755145	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	174449	9.9%
e	139615	8.0%
i	137124	7.8%
n	129507	7.4%
t	117560	6.7%
a	113948	6.5%
o	107694	6.1%
;	89057	5.1%
s	85286	4.9%
r	84748	4.8%
Other values (105)	576157	32.8%

abstract
Text

Missing

Distinct	9997
Distinct (%)	98.9%
Missing	724
Missing (%)	6.7%
Memory size	30.7 MiB

Length

Max length	13301
Median length	2092
Mean length	1328.3593
Min length	23

Characters and Unicode

Total characters	13425727
Distinct characters	426
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	9990 ?
Unique (%)	98.8%

Sample

1st row	Thinking Machines' CM-5 machine is a distributed-memory, message-passing computer. In the paper we devise a performance benchmark for the base and vector units and the data communication networks of the CM-5 machine. We model the communication characteristics such as communication latency and bandwidths of point-to-point and global communication primitives. We show, on a simple Gaussian elimination code, that an accurate static performance estimation of parallel algorithms is possible by using those basic machine properties connected with computation, vectorization, communication and synchronization. Furthermore, we describe the embedding of meshes or hypercubes on the CM-5 fat-tree topology and illustrate the performance results of their basic communication primitives.
2nd row	This article describes a computational modeling architecture, 4CAPS, which is consistent with key properties of cortical function and makes good contact with functional neuroimaging results. Like earlier cognitive models such as SOAR, ACT-R, 3CAPS, and EPIC, the proposed cognitive model is implemented in a computer simulation that predicts observable variables such as human response times and error patterns. In addition, the proposed 4CAPS model accounts for the functional decomposition of the cognitive system and predicts fMRI activation levels and their localization within specific cortical regions, by incorporating key properties of cortical function into the design of the modeling system.
3rd row	We present our numerical methods for the solution of large-scale incompressible flow applications with complex geometries. These methods include a stabilized finite element formulation of the Navier-Stokes equations, implementation of this formulation on parallel architectures such as the Thinking Machines CM-5 and the CRAY T3D, and automatic 3D mesh generation techniques based on Delaunay-Voronoï methods for the discretization of complex domains. All three of these methods are required for the numerical simulation of most engineering applications involving fluid flow. We apply these methods to the simulation of airflow past an automobile and fluid-particle interactions. The simulation of airflow past an automobile is of very large scale with a high level of detail and yielded many interesting airflow patterns which help in understanding the aerodynamic characteristics of such vehicles. © 1997 by John Wiley & Sons, Ltd.
4th row	In the near future, large ram-air parachutes are expected to provide the capability of delivering 21 ton pay loads from altitudes as high as 25,000 ft. In development and test and evaluation of these parachutes the size of the parachute needed and the deployment stages involved make high-performance computing (HPC) simulations a desirable alternative to costly airdrop tests. Although computational simulations based on realistic, 3D, time-dependent models will continue to be a major computational challenge, advanced finite element simulation techniques recently developed for this purpose and the execution of these techniques on HPC platforms are significant steps in the direction to meet this challenge. In this paper, two approaches for analysis of the inflation and gliding of ram-air parachutes are presented. In one of the approaches the point mass flight mechanics equations are solved with the time-varying drag and lift areas obtained from empirical data. This approach is limited to parachutes with similar configurations to those for which data are available. The other approach is 3D finite element computations based on the Navier-Stokes equations governing the airflow around the parachute canopy and Newton's law of motion governing the 3D dynamics of the canopy, with the forces acting on the canopy calculated from the simulated flow field. At the earlier stages of canopy inflation the parachute is modelled as an expanding box, whereas at the later stages, as it expands, the box transforms to a parafoil and glides. These finite element computations are carried out on the massively parallel supercomputers CRAY T3D and Thinking Machines CM-5, typically with millions of coupled, non-linear finite element equations solved simultaneously at every time step or pseudo-time step of the simulation. © 1997 by John Wiley & Sons, Ltd.
5th row	Massively parallel finite element methods for large-scale computation of storm surges and tidal flows are discussed here. The finite element computations, carried out using unstructured grids, are based on a three-step explicit formulation and on an implicit space-time formulation. Parallel implementations of these unstructured grid-based formulations are carried out on the Fujitsu Highly Parallel Computer AP1000 and on the Thinking Machines CM-5. Simulations of the storm surge accompanying the Ise-Bay typhoon in 1959 and of the tidal flow in Tokyo Bay serve as numerical examples. The impact of parallelization on this type of simulation is also investigated. The present methods are shown to be useful and powerful tools for the analysis of storm surges and tidal flows. © 1997 by John Wiley & Sons, Ltd.

Value	Count	Frequency (%)
the	108742	5.7%
and	75116	3.9%
of	72726	3.8%
to	49923	2.6%
in	47752	2.5%
a	36450	1.9%
for	20305	1.1%
that	19229	1.0%
is	18454	1.0%
this	18326	1.0%
Other values (51028)	1456269	75.7%

Most occurring characters

Value	Count	Frequency (%)
	1912617	14.2%
e	1280469	9.5%
t	983849	7.3%
i	928495	6.9%
n	873567	6.5%
a	853662	6.4%
o	794139	5.9%
s	736166	5.5%
r	653733	4.9%
c	463412	3.5%
Other values (416)	3945618	29.4%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	13425727	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	1912617	14.2%
e	1280469	9.5%
t	983849	7.3%
i	928495	6.9%
n	873567	6.5%
a	853662	6.4%
o	794139	5.9%
s	736166	5.5%
r	653733	4.9%
c	463412	3.5%
Other values (416)	3945618	29.4%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	13425727	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	1912617	14.2%
e	1280469	9.5%
t	983849	7.3%
i	928495	6.9%
n	873567	6.5%
a	853662	6.4%
o	794139	5.9%
s	736166	5.5%
r	653733	4.9%
c	463412	3.5%
Other values (416)	3945618	29.4%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	13425727	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	1912617	14.2%
e	1280469	9.5%
t	983849	7.3%
i	928495	6.9%
n	873567	6.5%
a	853662	6.4%
o	794139	5.9%
s	736166	5.5%
r	653733	4.9%
c	463412	3.5%
Other values (416)	3945618	29.4%

cited_by_scopus
Real number (ℝ)

High correlation Missing Skewed Zeros

Distinct	306
Distinct (%)	3.0%
Missing	724
Missing (%)	6.7%
Infinite	0
Infinite (%)	0.0%
Mean	21.363411

Minimum	0
Maximum	10043
Zeros	2301
Zeros (%)	21.2%
Negative	0
Negative (%)	0.0%
Memory size	84.7 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	1
median	4
Q3	14
95-th percentile	77.7
Maximum	10043
Range	10043
Interquartile range (IQR)	13

Descriptive statistics

Standard deviation	140.28213
Coefficient of variation (CV)	6.5664665
Kurtosis	2874.1411
Mean	21.363411
Median Absolute Deviation (MAD)	4
Skewness	45.895225
Sum	215920
Variance	19679.075
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0	2301	21.2%
1	1201	11.1%
2	793	7.3%
3	613	5.7%
4	502	4.6%
5	379	3.5%
6	308	2.8%
7	264	2.4%
8	249	2.3%
9	226	2.1%
Other values (296)	3271	30.2%
(Missing)	724	6.7%

Value	Count	Frequency (%)
0	2301	21.2%
1	1201	11.1%
2	793	7.3%
3	613	5.7%
4	502	4.6%
5	379	3.5%
6	308	2.8%
7	264	2.4%
8	249	2.3%
9	226	2.1%

Value	Count	Frequency (%)
10043	1	< 0.1%
5509	1	< 0.1%
3161	1	< 0.1%
3156	1	< 0.1%
2123	1	< 0.1%
1908	1	< 0.1%
1703	1	< 0.1%
1438	1	< 0.1%
1317	1	< 0.1%
1306	1	< 0.1%

references_count_scopus
Text

Missing

Distinct	9792
Distinct (%)	99.6%
Missing	1001
Missing (%)	9.2%
Memory size	18.5 MiB

Length

Max length	2254
Median length	1423
Mean length	1063.7052
Min length	9

Characters and Unicode

Total characters	10456222
Distinct characters	260
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	9759 ?
Unique (%)	99.3%

Sample

1st row	Solving Problems on Concurrent Processors, (1988); Hypercube Algorithms with Applications to Image Processing and Pattern Recognition, (1990); Bomans, Luc, Benchmarking the iPSC/2 hypercube multiprocessor, Concurrency Practice and Experience, 1, 1, pp. 3-18, (1989); Proceedings of the Frontiers of Massively Parallel Computation, (1992); Hockney, Roger W., Performance parameters and benchmarking of supercomputers, Parallel Computing, 17, 10-11, pp. 1111-1130, (1991); Kwan, Thomas T., Communication and computation performance of the CM-5, pp. 192-201, (1993); Leiserson, Charles E., Network architecture of the Connection Machine CM-5, pp. 272-285, (1992); Lin, Mengjou, Performance evaluation of the CM-5 interconnection network, pp. 189-198, (1993); Ponnusamy, Ravi, Experimental performance evaluation of the CM-5, Journal of Parallel and Distributed Computing, 19, 3, pp. 192-202, (1993); Bailey, David H., The nas parallel benchmarks, International Journal of High Performance Computing Applications, 5, 3, pp. 63-73, (1991)
2nd row	Rules of the Mind, (1993); Awh, Edward, Dissociation of Storage and Rehearsal in Verbal Working Memory: Evidence from Positron Emission Tomography, Psychological Science, 7, 1, pp. 25-31, (1996); Agrammatism, (1985); Carpenter, Patricia Ann, Graded functional activation in the visuospatial system with the amount of task demand, Journal of Cognitive Neuroscience, 11, 1, pp. 9-24, (1999); Carpenter, Patricia Ann, What one intelligence test measures: A theoretical account of the processing in the Raven progressive matrices test, Psychological Review, 97, 3, pp. 404-431, (1990); Collins, Allan M., Retrieval time from semantic memory, Journal of Verbal Learning and Verbal Behavior, 8, 2, pp. 240-247, (1969); Rethinking Innateness A Connectionist Perspective on Development, (1996); Human Brain Function, (1997); Gabrieli, John D.E., The role of left prefrontal cortex in language and memory, Proceedings of the National Academy of Sciences of the United States of America, 95, 3, pp. 906-913, (1998); Grafman, Jordan Henry, Similarities and Distinctions among Current Models of Prefrontal Cortical Functions, Annals of the New York Academy of Sciences, 769, 1, pp. 337-368, (1995)
3rd row	Tezduyar, Tayfun E., Computation of unsteady incompressible flows with the stabilized finite element methods: Space-time formulations, iterative strategies and massively parallel implementations, American Society of Mechanical Engineers, Pressure Vessels and Piping Division (Publication) PVP, 246, pp. 7-24, (1992); Añón, J. C R, Computation of incompressible flows with implicit finite element implementations on the Connection Machine, Computer Methods in Applied Mechanics and Engineering, 108, 1-2, pp. 99-118, (1993); Tezduyar, Tayfun E., Parallel Finite-Element Computation of 3D Flows, Computer, 26, 10, pp. 27-36, (1993); Computational Mechanics 95 Proc Int Conf on Computational Engineering Science, (1995); Pvm Parallel Virtual Machine, (1994); Añón, J. C R, An efficient communications strategy for finite element methods on the Connection Machine CM-5 system, Computer Methods in Applied Mechanics and Engineering, 113, 3-4, pp. 363-387, (1994); Mesh Generation and Update Strategies for Parallel Computation of Flow Problems with Moving Boundaries and Interfaces, (1995); Technical Report, (1995); Aliabadi, Shabrouz K., Parallel fluid dynamics computations in aerospace applications, International Journal for Numerical Methods in Fluids, 21, 10, pp. 783-805, (1995); Tezduyar, Tayfun E., Massively parallel finite element simulation of compressible and incompressible flows, Computer Methods in Applied Mechanics and Engineering, 119, 1-2, pp. 157-177, (1994)
4th row	Añón, J. C R, Development testing of large ram air inflated wings, (1993); Garrard, William L., Inflation analysis of ram air inflated gliding parachutes, pp. 186-198, (1995); Aliabadi, Shahrouz K., Parallel finite element computation of the dynamics of large ram air parachutes, pp. 278-293, (1995); Añón, J. C R, SEMI-EMPIRICAL THEORY TO PREDICT THE LOAD-TIME HISTORY OF AN INFLATING PARACHUTE., pp. 177-185, (1984); University of Minnesota Parachute Systems Technology Short Courses, (1994); AIAA Paper 91 0862, (1991); Tezduyar, Tayfun E., Parallel Finite-Element Computation of 3D Flows, Computer, 26, 10, pp. 27-36, (1993); Tezduyar, Tayfun E., Massively parallel finite element simulation of compressible and incompressible flows, Computer Methods in Applied Mechanics and Engineering, 119, 1-2, pp. 157-177, (1994); Tezduyar, Tayfun E., A new strategy for finite element computations involving moving boundaries and interfaces-The deforming-spatial-domain/space-time procedure: I. The concept and the preliminary numerical tests, Computer Methods in Applied Mechanics and Engineering, 94, 3, pp. 339-351, (1992); Tezduyar, Tayfun E., A new strategy for finite element computations involving moving boundaries and interfaces-The deforming-spatial-domain/space-time procedure: II. Computation of free-surface flows, two-liquid flows, and flows with drifting cylinders, Computer Methods in Applied Mechanics and Engineering, 94, 3, pp. 353-371, (1992)
5th row	Añón, J. C R, Tide and storm surge predictions using finite element model, Journal of Hydraulic Engineering, 118, 10, pp. 1373-1390, (1992); Añón, J. C R, Massively parallel finite element method for large scale computation of storm surge, 2, pp. 79-86, (1996); Añón, J. C R, Three‐step explicit finite element computation of shallow water flows on a massively parallel computer, International Journal for Numerical Methods in Fluids, 21, 10, pp. 885-900, (1995); Añón, J. C R, Selective lumping finite element method for shallow water flow, International Journal for Numerical Methods in Fluids, 2, 1, pp. 89-112, (1982); Tezduyar, Tayfun E., Computation of unsteady incompressible flows with the stabilized finite element methods: Space-time formulations, iterative strategies and massively parallel implementations, American Society of Mechanical Engineers, Pressure Vessels and Piping Division (Publication) PVP, 246, pp. 7-24, (1992); Hughes, Thomas J.R., A multi-dimensioal upwind scheme with no crosswind diffusion., 34 )., pp. 19-35, (1979); Tezduyar, Tayfun E., FINITE ELEMENT FORMULATIONS FOR CONVECTION DOMINATED FLOWS WITH PARTICULAR EMPHASIS ON THE COMPRESSIBLE EULER EQUATIONS., (1983); Añón, J. C R, Finite element computation of compressible flows with the SUPG formulation, American Society of Mechanical Engineers, Fluids Engineering Division (Publication) FEDSM, 123, pp. 21-27, (1991); Añón, J. C R, SUPG finite element computation of compressible flows with the entropy and conservation variables formulations, Computer Methods in Applied Mechanics and Engineering, 104, 3, pp. 397-422, (1993); Aliabadi, Shabrouz K., SUPG finite element computation of viscous compressible flows based on the conservation and entropy variables formulations, Computational Mechanics, 11, 5-6, pp. 300-312, (1993)

Value	Count	Frequency (%)
of	54578	3.9%
and	46033	3.3%
pp	44781	3.2%
the	32471	2.3%
in	31804	2.3%
a	21555	1.5%
education	19887	1.4%
for	16294	1.2%
thinking	14857	1.1%
computational	14605	1.0%
Other values (83956)	1109855	78.9%

Most occurring characters

Value	Count	Frequency (%)
	1396716	13.4%
e	699321	6.7%
n	680439	6.5%
i	625728	6.0%
a	586073	5.6%
o	568802	5.4%
t	499713	4.8%
r	419513	4.0%
,	374416	3.6%
s	342841	3.3%
Other values (250)	4262660	40.8%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	10456222	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	1396716	13.4%
e	699321	6.7%
n	680439	6.5%
i	625728	6.0%
a	586073	5.6%
o	568802	5.4%
t	499713	4.8%
r	419513	4.0%
,	374416	3.6%
s	342841	3.3%
Other values (250)	4262660	40.8%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	10456222	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	1396716	13.4%
e	699321	6.7%
n	680439	6.5%
i	625728	6.0%
a	586073	5.6%
o	568802	5.4%
t	499713	4.8%
r	419513	4.0%
,	374416	3.6%
s	342841	3.3%
Other values (250)	4262660	40.8%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	10456222	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	1396716	13.4%
e	699321	6.7%
n	680439	6.5%
i	625728	6.0%
a	586073	5.6%
o	568802	5.4%
t	499713	4.8%
r	419513	4.0%
,	374416	3.6%
s	342841	3.3%
Other values (250)	4262660	40.8%

publisher_scopus
Text

Missing

Distinct	880
Distinct (%)	9.5%
Missing	1611
Missing (%)	14.9%
Memory size	800.9 KiB

Length

Max length	130
Median length	107
Mean length	34.30987
Min length	3

Characters and Unicode

Total characters	316337
Distinct characters	80
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	480 ?
Unique (%)	5.2%

Sample

1st row	John Wiley and Sons Ltd
2nd row	John Wiley and Sons Ltd
3rd row	John Wiley and Sons Ltd
4th row	John Wiley and Sons Ltd
5th row	John Wiley and Sons Ltd

Value	Count	Frequency (%)
and	2674	6.7%
inc	2138	5.4%
of	1892	4.8%
springer	1735	4.4%
institute	1586	4.0%
association	1424	3.6%
for	1400	3.5%
engineers	1200	3.0%
computing	1161	2.9%
machinery	1158	2.9%
Other values (1453)	23334	58.8%

Most occurring characters

Value	Count	Frequency (%)
	30482	9.6%
i	26505	8.4%
n	25571	8.1%
e	24984	7.9%
r	17878	5.7%
c	17832	5.6%
t	16783	5.3%
s	16775	5.3%
a	16284	5.1%
o	15692	5.0%
Other values (70)	107551	34.0%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	316337	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	30482	9.6%
i	26505	8.4%
n	25571	8.1%
e	24984	7.9%
r	17878	5.7%
c	17832	5.6%
t	16783	5.3%
s	16775	5.3%
a	16284	5.1%
o	15692	5.0%
Other values (70)	107551	34.0%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	316337	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	30482	9.6%
i	26505	8.4%
n	25571	8.1%
e	24984	7.9%
r	17878	5.7%
c	17832	5.6%
t	16783	5.3%
s	16775	5.3%
a	16284	5.1%
o	15692	5.0%
Other values (70)	107551	34.0%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	316337	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	30482	9.6%
i	26505	8.4%
n	25571	8.1%
e	24984	7.9%
r	17878	5.7%
c	17832	5.6%
t	16783	5.3%
s	16775	5.3%
a	16284	5.1%
o	15692	5.0%
Other values (70)	107551	34.0%

language_scopus
Categorical

High correlation Imbalance Missing

Distinct	16
Distinct (%)	0.2%
Missing	724
Missing (%)	6.7%
Memory size	592.6 KiB

English	9801
Spanish	128
Chinese	83
Portuguese	45
Russian	19
Other values (11)	31

Length

Max length	10
Median length	7
Mean length	7.0124666
Min length	6

Characters and Unicode

Total characters	70875
Distinct characters	30
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	2 ?
Unique (%)	< 0.1%

Sample

1st row	English
2nd row	English
3rd row	English
4th row	English
5th row	English

Common Values

Value	Count	Frequency (%)
English	9801	90.5%
Spanish	128	1.2%
Chinese	83	0.8%
Portuguese	45	0.4%
Russian	19	0.2%
German	5	< 0.1%
Polish	4	< 0.1%
Italian	4	< 0.1%
Croatian	3	< 0.1%
French	3	< 0.1%
Other values (6)	12	0.1%
(Missing)	724	6.7%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
english	9801	97.0%
spanish	128	1.3%
chinese	83	0.8%
portuguese	45	0.4%
russian	19	0.2%
german	5	< 0.1%
polish	4	< 0.1%
italian	4	< 0.1%
croatian	3	< 0.1%
french	3	< 0.1%
Other values (6)	12	0.1%

Most occurring characters

Value	Count	Frequency (%)
s	10105	14.3%
n	10054	14.2%
i	10049	14.2%
h	10021	14.1%
g	9847	13.9%
l	9809	13.8%
E	9801	13.8%
e	273	0.4%
a	180	0.3%
p	131	0.2%
Other values (20)	605	0.9%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	70875	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
s	10105	14.3%
n	10054	14.2%
i	10049	14.2%
h	10021	14.1%
g	9847	13.9%
l	9809	13.8%
E	9801	13.8%
e	273	0.4%
a	180	0.3%
p	131	0.2%
Other values (20)	605	0.9%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	70875	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
s	10105	14.3%
n	10054	14.2%
i	10049	14.2%
h	10021	14.1%
g	9847	13.9%
l	9809	13.8%
E	9801	13.8%
e	273	0.4%
a	180	0.3%
p	131	0.2%
Other values (20)	605	0.9%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	70875	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
s	10105	14.3%
n	10054	14.2%
i	10049	14.2%
h	10021	14.1%
g	9847	13.9%
l	9809	13.8%
E	9801	13.8%
e	273	0.4%
a	180	0.3%
p	131	0.2%
Other values (20)	605	0.9%

affiliations_scopus
Text

Missing

Distinct	8257
Distinct (%)	82.9%
Missing	872
Missing (%)	8.1%
Memory size	2.5 MiB

Length

Max length	2979
Median length	610
Mean length	155.70509
Min length	6

Characters and Unicode

Total characters	1550667
Distinct characters	166
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	7396 ?
Unique (%)	74.3%

Sample

1st row	Northeast Parallel Architectures Center, Syracuse University, Syracuse, NY, United States
2nd row	Center for Cognitive Brain Imaging, Carnegie Mellon University, Pittsburgh, PA, United States; Department of Psychology, Carnegie Mellon University, Pittsburgh, PA, United States
3rd row	Army HPC Research Center, College of Science and Engineering, Minneapolis, MN, United States
4th row	Army HPC Research Center, College of Science and Engineering, Minneapolis, MN, United States; Indian Institute of Technology Kanpur, Kanpur, UP, India; U.S. Army Natick RD and E Center, Natick, MA, United States
5th row	Department of Civil Engineering, Chuo University, Hachioji, Tokyo, Japan; University of Minnesota Twin Cities, Minneapolis, MN, United States

Value	Count	Frequency (%)
of	14845	7.6%
university	9841	5.0%
united	6752	3.5%
states	5816	3.0%
department	5189	2.7%
and	4565	2.3%
de	2667	1.4%
education	2514	1.3%
china	2344	1.2%
science	2328	1.2%
Other values (9495)	138073	70.8%

Most occurring characters

Value	Count	Frequency (%)
	184972	11.9%
e	124190	8.0%
n	114070	7.4%
i	111090	7.2%
a	110084	7.1%
t	93890	6.1%
o	81846	5.3%
,	68646	4.4%
r	67966	4.4%
s	53874	3.5%
Other values (156)	540039	34.8%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	1550667	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	184972	11.9%
e	124190	8.0%
n	114070	7.4%
i	111090	7.2%
a	110084	7.1%
t	93890	6.1%
o	81846	5.3%
,	68646	4.4%
r	67966	4.4%
s	53874	3.5%
Other values (156)	540039	34.8%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	1550667	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	184972	11.9%
e	124190	8.0%
n	114070	7.4%
i	111090	7.2%
a	110084	7.1%
t	93890	6.1%
o	81846	5.3%
,	68646	4.4%
r	67966	4.4%
s	53874	3.5%
Other values (156)	540039	34.8%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	1550667	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	184972	11.9%
e	124190	8.0%
n	114070	7.4%
i	111090	7.2%
a	110084	7.1%
t	93890	6.1%
o	81846	5.3%
,	68646	4.4%
r	67966	4.4%
s	53874	3.5%
Other values (156)	540039	34.8%

country_scopus
Unsupported

Missing Rejected Unsupported

Missing	10831
Missing (%)	100.0%
Memory size	84.7 KiB

source_scopus
Boolean

Constant Missing

Distinct	1
Distinct (%)	< 0.1%
Missing	724
Missing (%)	6.7%
Memory size	378.1 KiB

True	10107
(Missing)	724

Value	Count	Frequency (%)
True	10107	93.3%
(Missing)	724	6.7%

title_wos
Text

Missing

Distinct	4186
Distinct (%)	99.9%
Missing	6639
Missing (%)	61.3%
Memory size	817.6 KiB

Length

Max length	242
Median length	168
Mean length	99.683445
Min length	6

Characters and Unicode

Total characters	417873
Distinct characters	91
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	4180 ?
Unique (%)	99.7%

Sample

1st row	The impact of inducing troubleshooting strategies via visual aids on performance in a computerized digital network task
2nd row	Insights into computational thinking from think-aloud interviews: A systematic review
3rd row	Student responses to creative coding in biomedical science education
4th row	Modeling and Simulation Practices for a Computational Thinking-Enabled Engineering Workforce
5th row	Modeling and simulation practices in engineering education

Value	Count	Frequency (%)
computational	2299	4.3%
thinking	2262	4.2%
of	2135	4.0%
and	2096	3.9%
in	2036	3.8%
a	1626	3.0%
the	1444	2.7%
for	1063	2.0%
learning	927	1.7%
to	846	1.6%
Other values (5090)	36599	68.6%

Most occurring characters

Value	Count	Frequency (%)
	49141	11.8%
n	33500	8.0%
i	33140	7.9%
e	30281	7.2%
t	27565	6.6%
o	27402	6.6%
a	26956	6.5%
r	18877	4.5%
s	15907	3.8%
l	13987	3.3%
Other values (81)	141117	33.8%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	417873	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	49141	11.8%
n	33500	8.0%
i	33140	7.9%
e	30281	7.2%
t	27565	6.6%
o	27402	6.6%
a	26956	6.5%
r	18877	4.5%
s	15907	3.8%
l	13987	3.3%
Other values (81)	141117	33.8%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	417873	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	49141	11.8%
n	33500	8.0%
i	33140	7.9%
e	30281	7.2%
t	27565	6.6%
o	27402	6.6%
a	26956	6.5%
r	18877	4.5%
s	15907	3.8%
l	13987	3.3%
Other values (81)	141117	33.8%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	417873	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	49141	11.8%
n	33500	8.0%
i	33140	7.9%
e	30281	7.2%
t	27565	6.6%
o	27402	6.6%
a	26956	6.5%
r	18877	4.5%
s	15907	3.8%
l	13987	3.3%
Other values (81)	141117	33.8%

year_wos
Real number (ℝ)

High correlation Missing

Distinct	23
Distinct (%)	0.5%
Missing	6639
Missing (%)	61.3%
Infinite	0
Infinite (%)	0.0%
Mean	2021.584

Minimum	1994
Maximum	2026
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	84.7 KiB

Quantile statistics

Minimum	1994
5-th percentile	2017
Q1	2020
median	2022
Q3	2024
95-th percentile	2025
Maximum	2026
Range	32
Interquartile range (IQR)	4

Descriptive statistics

Standard deviation	2.9696003
Coefficient of variation (CV)	0.0014689473
Kurtosis	4.6535253
Mean	2021.584
Median Absolute Deviation (MAD)	2
Skewness	-1.3642788
Sum	8474480
Variance	8.8185261
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=23)

Value	Count	Frequency (%)
2025	669	6.2%
2024	609	5.6%
2023	539	5.0%
2021	523	4.8%
2022	522	4.8%
2020	398	3.7%
2019	322	3.0%
2018	199	1.8%
2017	173	1.6%
2016	59	0.5%
Other values (13)	179	1.7%
(Missing)	6639	61.3%

Value	Count	Frequency (%)
1994	1	< 0.1%
1995	1	< 0.1%
2006	1	< 0.1%
2007	2	< 0.1%
2008	3	< 0.1%
2009	11	0.1%
2010	5	< 0.1%
2011	7	0.1%
2012	10	0.1%
2013	19	0.2%

Value	Count	Frequency (%)
2026	32	0.3%
2025	669	6.2%
2024	609	5.6%
2023	539	5.0%
2022	522	4.8%
2021	523	4.8%
2020	398	3.7%
2019	322	3.0%
2018	199	1.8%
2017	173	1.6%

journal_wos
Text

Missing

Distinct	1234
Distinct (%)	29.4%
Missing	6639
Missing (%)	61.3%
Memory size	623.1 KiB

Length

Max length	242
Median length	116
Mean length	52.488311
Min length	2

Characters and Unicode

Total characters	220031
Distinct characters	71
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	747 ?
Unique (%)	17.8%

Sample

1st row	APPLIED COGNITIVE PSYCHOLOGY
2nd row	APPLIED COGNITIVE PSYCHOLOGY
3rd row	BIOCHEMISTRY AND MOLECULAR BIOLOGY EDUCATION
4th row	COMPUTER APPLICATIONS IN ENGINEERING EDUCATION
5th row	COMPUTER APPLICATIONS IN ENGINEERING EDUCATION

Value	Count	Frequency (%)
education	1955	6.8%
of	1574	5.4%
and	1390	4.8%
on	1096	3.8%
in	989	3.4%
conference	882	3.1%
the	812	2.8%
proceedings	756	2.6%
journal	715	2.5%
science	657	2.3%
Other values (1513)	18077	62.5%

Most occurring characters

Value	Count	Frequency (%)
	24711	11.2%
E	21982	10.0%
N	20803	9.5%
I	17119	7.8%
O	16540	7.5%
C	14756	6.7%
A	13545	6.2%
T	13110	6.0%
R	9652	4.4%
S	8667	3.9%
Other values (61)	59146	26.9%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	220031	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	24711	11.2%
E	21982	10.0%
N	20803	9.5%
I	17119	7.8%
O	16540	7.5%
C	14756	6.7%
A	13545	6.2%
T	13110	6.0%
R	9652	4.4%
S	8667	3.9%
Other values (61)	59146	26.9%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	220031	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	24711	11.2%
E	21982	10.0%
N	20803	9.5%
I	17119	7.8%
O	16540	7.5%
C	14756	6.7%
A	13545	6.2%
T	13110	6.0%
R	9652	4.4%
S	8667	3.9%
Other values (61)	59146	26.9%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	220031	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	24711	11.2%
E	21982	10.0%
N	20803	9.5%
I	17119	7.8%
O	16540	7.5%
C	14756	6.7%
A	13545	6.2%
T	13110	6.0%
R	9652	4.4%
S	8667	3.9%
Other values (61)	59146	26.9%

document_type_wos
Categorical

High correlation Imbalance Missing

Distinct	19
Distinct (%)	0.5%
Missing	6639
Missing (%)	61.3%
Memory size	610.9 KiB

Article	2160
Proceedings Paper	1591
Review	190
Article; Early Access	95
Article; Book Chapter	59
Other values (14)	97

Length

Max length	32
Median length	7
Mean length	11.496899
Min length	4

Characters and Unicode

Total characters	48195
Distinct characters	32
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	6 ?
Unique (%)	0.1%

Sample

1st row	Article
2nd row	Review
3rd row	Article
4th row	Article
5th row	Article

Common Values

Value	Count	Frequency (%)
Article	2160	19.9%
Proceedings Paper	1591	14.7%
Review	190	1.8%
Article; Early Access	95	0.9%
Article; Book Chapter	59	0.5%
Editorial Material	40	0.4%
Correction	13	0.1%
Meeting Abstract	12	0.1%
Article; Proceedings Paper	11	0.1%
Review; Early Access	6	0.1%
Other values (9)	15	0.1%
(Missing)	6639	61.3%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
article	2326	37.6%
proceedings	1602	25.9%
paper	1602	25.9%
review	202	3.3%
early	102	1.6%
access	102	1.6%
book	67	1.1%
chapter	61	1.0%
editorial	42	0.7%
material	42	0.7%
Other values (8)	44	0.7%

Most occurring characters

Value	Count	Frequency (%)
e	7788	16.2%
r	5821	12.1%
i	4284	8.9%
c	4159	8.6%
P	3205	6.7%
t	2531	5.3%
l	2513	5.2%
A	2440	5.1%
	2000	4.1%
a	1906	4.0%
Other values (22)	11548	24.0%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	48195	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
e	7788	16.2%
r	5821	12.1%
i	4284	8.9%
c	4159	8.6%
P	3205	6.7%
t	2531	5.3%
l	2513	5.2%
A	2440	5.1%
	2000	4.1%
a	1906	4.0%
Other values (22)	11548	24.0%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	48195	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
e	7788	16.2%
r	5821	12.1%
i	4284	8.9%
c	4159	8.6%
P	3205	6.7%
t	2531	5.3%
l	2513	5.2%
A	2440	5.1%
	2000	4.1%
a	1906	4.0%
Other values (22)	11548	24.0%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	48195	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
e	7788	16.2%
r	5821	12.1%
i	4284	8.9%
c	4159	8.6%
P	3205	6.7%
t	2531	5.3%
l	2513	5.2%
A	2440	5.1%
	2000	4.1%
a	1906	4.0%
Other values (22)	11548	24.0%

publication_type_wos
Categorical

High correlation Missing

Distinct	4
Distinct (%)	0.1%
Missing	6639
Missing (%)	61.3%
Memory size	567.9 KiB

J	2526
C	1591
B	52
S	23

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	4192
Distinct characters	4
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	J
2nd row	J
3rd row	J
4th row	J
5th row	J

Common Values

Value	Count	Frequency (%)
J	2526	23.3%
C	1591	14.7%
B	52	0.5%
S	23	0.2%
(Missing)	6639	61.3%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
j	2526	60.3%
c	1591	38.0%
b	52	1.2%
s	23	0.5%

Most occurring characters

Value	Count	Frequency (%)
J	2526	60.3%
C	1591	38.0%
B	52	1.2%
S	23	0.5%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	4192	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
J	2526	60.3%
C	1591	38.0%
B	52	1.2%
S	23	0.5%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	4192	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
J	2526	60.3%
C	1591	38.0%
B	52	1.2%
S	23	0.5%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	4192	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
J	2526	60.3%
C	1591	38.0%
B	52	1.2%
S	23	0.5%

authors_wos
Text

Missing

Distinct	3880
Distinct (%)	92.6%
Missing	6640
Missing (%)	61.3%
Memory size	615.1 KiB

Length

Max length	396
Median length	140
Mean length	39.709139
Min length	5

Characters and Unicode

Total characters	166421
Distinct characters	77
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	3663 ?
Unique (%)	87.4%

Sample

1st row	Bordewieck, M; Elson, M
2nd row	Pan, ZX; Cui, Y; Leighton, JP; Cutumisu, M
3rd row	Gough, P; Bown, O; Campbell, CR; Poronnik, P; Ross, PM
4th row	Magana, AJ; Coutinho, GS
5th row	Magana, AJ; de Jong, T

Value	Count	Frequency (%)
m	1011	3.5%
a	940	3.2%
j	709	2.4%
s	692	2.4%
c	608	2.1%
d	505	1.7%
e	435	1.5%
r	424	1.4%
k	407	1.4%
l	388	1.3%
Other values (7385)	23147	79.1%

Most occurring characters

Value	Count	Frequency (%)
	25075	15.1%
,	14498	8.7%
a	10763	6.5%
;	10335	6.2%
e	8379	5.0%
n	7264	4.4%
i	6546	3.9%
o	6328	3.8%
r	6247	3.8%
l	4301	2.6%
Other values (67)	66685	40.1%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	166421	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	25075	15.1%
,	14498	8.7%
a	10763	6.5%
;	10335	6.2%
e	8379	5.0%
n	7264	4.4%
i	6546	3.9%
o	6328	3.8%
r	6247	3.8%
l	4301	2.6%
Other values (67)	66685	40.1%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	166421	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	25075	15.1%
,	14498	8.7%
a	10763	6.5%
;	10335	6.2%
e	8379	5.0%
n	7264	4.4%
i	6546	3.9%
o	6328	3.8%
r	6247	3.8%
l	4301	2.6%
Other values (67)	66685	40.1%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	166421	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	25075	15.1%
,	14498	8.7%
a	10763	6.5%
;	10335	6.2%
e	8379	5.0%
n	7264	4.4%
i	6546	3.9%
o	6328	3.8%
r	6247	3.8%
l	4301	2.6%
Other values (67)	66685	40.1%

author_full_names_wos
Text

Missing

Distinct	3897
Distinct (%)	93.0%
Missing	6640
Missing (%)	61.3%
Memory size	656.3 KiB

Length

Max length	568
Median length	179
Mean length	60.631114
Min length	8

Characters and Unicode

Total characters	254105
Distinct characters	60
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	3693 ?
Unique (%)	88.1%

Sample

1st row	Bordewieck, Martin; Elson, Malte
2nd row	Pan, Zexuan; Cui, Ying; Leighton, Jacqueline P.; Cutumisu, Maria
3rd row	Gough, Phillip; Bown, Oliver; Campbell, Craig R.; Poronnik, Philip; Ross, Pauline M.
4th row	Magana, Alejandra J.; Coutinho, Genisson Silva
5th row	Magana, Alejandra J.; de Jong, Ton

Value	Count	Frequency (%)
m	229	0.7%
a	208	0.6%
wang	161	0.5%
j	153	0.5%
chen	149	0.4%
maria	144	0.4%
c	133	0.4%
li	132	0.4%
jose	129	0.4%
zhang	120	0.4%
Other values (11560)	31763	95.3%

Most occurring characters

Value	Count	Frequency (%)
	29130	11.5%
a	24247	9.5%
e	16655	6.6%
i	16516	6.5%
n	16447	6.5%
,	14508	5.7%
r	12137	4.8%
o	11864	4.7%
;	10335	4.1%
l	8358	3.3%
Other values (50)	93908	37.0%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	254105	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	29130	11.5%
a	24247	9.5%
e	16655	6.6%
i	16516	6.5%
n	16447	6.5%
,	14508	5.7%
r	12137	4.8%
o	11864	4.7%
;	10335	4.1%
l	8358	3.3%
Other values (50)	93908	37.0%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	254105	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	29130	11.5%
a	24247	9.5%
e	16655	6.6%
i	16516	6.5%
n	16447	6.5%
,	14508	5.7%
r	12137	4.8%
o	11864	4.7%
;	10335	4.1%
l	8358	3.3%
Other values (50)	93908	37.0%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	254105	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	29130	11.5%
a	24247	9.5%
e	16655	6.6%
i	16516	6.5%
n	16447	6.5%
,	14508	5.7%
r	12137	4.8%
o	11864	4.7%
;	10335	4.1%
l	8358	3.3%
Other values (50)	93908	37.0%

author_keywords_wos
Text

Missing

Distinct	3871
Distinct (%)	99.4%
Missing	6935
Missing (%)	64.0%
Memory size	780.2 KiB

Length

Max length	871
Median length	215
Mean length	98.48922
Min length	3

Characters and Unicode

Total characters	383714
Distinct characters	78
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	3849 ?
Unique (%)	98.8%

Sample

1st row	computational thinking; networks; problem solving; troubleshooting; visual aids
2nd row	cognitive processes; computational thinking; think-aloud
3rd row	Arduino; biomedical science education; creative code; data visualization; processing
4th row	computational thinking; engineering education; modeling and simulation; science and engineering workforce
5th row	graduate; K-12; modeling; post-graduate; simulation

Value	Count	Frequency (%)
thinking	3068	7.9%
computational	2983	7.7%
education	1992	5.1%
learning	1358	3.5%
programming	1169	3.0%
science	654	1.7%
computer	549	1.4%
educational	421	1.1%
design	347	0.9%
robotics	345	0.9%
Other values (3648)	25895	66.8%

Most occurring characters

Value	Count	Frequency (%)
	34885	9.1%
i	33753	8.8%
n	30795	8.0%
t	27399	7.1%
e	27146	7.1%
a	26835	7.0%
o	24543	6.4%
r	16779	4.4%
c	16048	4.2%
;	15990	4.2%
Other values (68)	129541	33.8%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	383714	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	34885	9.1%
i	33753	8.8%
n	30795	8.0%
t	27399	7.1%
e	27146	7.1%
a	26835	7.0%
o	24543	6.4%
r	16779	4.4%
c	16048	4.2%
;	15990	4.2%
Other values (68)	129541	33.8%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	383714	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	34885	9.1%
i	33753	8.8%
n	30795	8.0%
t	27399	7.1%
e	27146	7.1%
a	26835	7.0%
o	24543	6.4%
r	16779	4.4%
c	16048	4.2%
;	15990	4.2%
Other values (68)	129541	33.8%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	383714	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	34885	9.1%
i	33753	8.8%
n	30795	8.0%
t	27399	7.1%
e	27146	7.1%
a	26835	7.0%
o	24543	6.4%
r	16779	4.4%
c	16048	4.2%
;	15990	4.2%
Other values (68)	129541	33.8%

keywords_plus_wos
Text

Missing

Distinct	2018
Distinct (%)	79.4%
Missing	8288
Missing (%)	76.5%
Memory size	499.5 KiB

Length

Max length	187
Median length	142
Mean length	47.801809
Min length	2

Characters and Unicode

Total characters	121560
Distinct characters	40
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	1900 ?
Unique (%)	74.7%

Sample

1st row	VERBAL REPORTS; SKILLS; GAME; EDUCATION; TEACHERS; LEARN
2nd row	COMPUTATIONAL THINKING; NARRATIVES; DESIGN; SKILLS; LEARN
3rd row	CURRICULUM; STUDENTS; SKILLS; MATHEMATICS; MOTIVATION; STANDARDS; SCIENCE
4th row	COMPUTATIONAL THINKING; SCIENCE; SYSTEMS; DESIGN; TOOLS; K-12
5th row	COMPUTATIONAL THINKING; AUTOMATED ASSESSMENT; ASSIGNMENTS

Value	Count	Frequency (%)
thinking	854	7.1%
computational	760	6.4%
education	443	3.7%
students	337	2.8%
science	323	2.7%
robotics	313	2.6%
k-12	294	2.5%
skills	292	2.4%
design	283	2.4%
mathematics	206	1.7%
Other values (1415)	7849	65.7%

Most occurring characters

Value	Count	Frequency (%)
E	10770	8.9%
I	10249	8.4%
T	9781	8.0%
	9411	7.7%
N	8760	7.2%
A	7596	6.2%
O	7575	6.2%
;	7322	6.0%
C	6687	5.5%
S	6591	5.4%
Other values (30)	36818	30.3%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	121560	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
E	10770	8.9%
I	10249	8.4%
T	9781	8.0%
	9411	7.7%
N	8760	7.2%
A	7596	6.2%
O	7575	6.2%
;	7322	6.0%
C	6687	5.5%
S	6591	5.4%
Other values (30)	36818	30.3%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	121560	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
E	10770	8.9%
I	10249	8.4%
T	9781	8.0%
	9411	7.7%
N	8760	7.2%
A	7596	6.2%
O	7575	6.2%
;	7322	6.0%
C	6687	5.5%
S	6591	5.4%
Other values (30)	36818	30.3%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	121560	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
E	10770	8.9%
I	10249	8.4%
T	9781	8.0%
	9411	7.7%
N	8760	7.2%
A	7596	6.2%
O	7575	6.2%
;	7322	6.0%
C	6687	5.5%
S	6591	5.4%
Other values (30)	36818	30.3%

abstract_wos
Text

Missing

Distinct	4106
Distinct (%)	99.9%
Missing	6720
Missing (%)	62.0%
Memory size	5.5 MiB

Length

Max length	22855
Median length	1736
Mean length	1300.9599
Min length	81

Characters and Unicode

Total characters	5348246
Distinct characters	92
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	4101 ?
Unique (%)	99.8%

Sample

1st row	Troubleshooting is a particular problem-solving process comprising error detection, fault diagnosis, and system restoration. Since automation of systems has become increasingly complex and ubiquitous, troubleshooting skills are crucial to maintain safety and security in a variety of contexts. The planned study aims at examining troubleshooting strategies and how to induce them by means of simple visual aids and concise instructions. To this end, a computerized task consisting of network troubleshooting problems will be employed in an experimental study with repeated measures. Indicators of strategy use and performance will be tested for their relation to availability and differential use of visual aids, to cognitive styles that affect how individuals deal with challenges or system information, and to cognitive processes that are involved in metacognition and executive function. The planned research is expected to help gain insights into the cognitive determinants of troubleshooting, reverse engineering, and their links to computational thinking.
2nd row	This systematic review examines 35 empirical studies featuring the use of think-aloud interviews in computational thinking (CT) research. Findings show that think-aloud interviews (1) are typically conducted in Computer Science classrooms and with K-12 students; (2) are usually combined with other exploratory CT assessment tools; (3) have the potential to benefit learners with special needs and identify the competency gaps through involving diverse participants; (4) are conducted in the absence of cognitive models and standard procedures; and (5) display insufficient definitional and methodological rigor. Theoretically, this review presents a systematic assessment about the application of think-aloud interviews in CT studies and identifies the limitations in existing CT-related think-aloud studies. Practically, this review serves as a reference for studying the cognitive processes during CT problem-solving and provides suggestions for CT researchers who intend to incorporate think-aloud interviews in their studies.
3rd row	Biomedical science students need to learn to code. Graduates face a future where they will be better prepared for research higher degrees and the workforce if they can code. Embedding coding in a biomedical curriculum comes with challenges. First, biomedical science students often experience anxiety learning quantitative and computational thinking skills and second biomedical faculty often lack expertise required to teach coding. In this study, we describe a creative coding approach to building coding skills in students using the packages of Processing and Arduino. Biomedical science students were taught by an interdisciplinary faculty team from Medicine and Health, Science and Architecture, Design and Planning. We describe quantitative and qualitative responses of students to this approach. Cluster analysis revealed a diversity of student responses, with a large majority of students who supported creative coding in the curriculum, a smaller but vocal cluster, who did not support creative coding because either the exercises were not sufficiently challenging or were too challenging and believed coding should not be in a Biomedical Science curriculum. We describe how two creative coding platforms, Processing and Arduino, embedded and used to visualize human physiological data, and provide responses to students, including those minority of students, who are opposed to coding in the curriculum This study found a variety of students responses in a final year capstone course of an undergraduate Biomedical Science degree where future pathways for students are either in research higher degrees or to the workforce with a future which will be increasingly data driven.
4th row	Computational thinking has been recognized as a collection of understandings and skills required for new generations of students not only proficient at using tools, but also at creating them and understanding the implication of their capabilities and limitations. This study proposes the combination of modeling and simulation practices along with disciplinary learning as a way to synergistically integrate and take advantage of computational thinking in engineering education. This paper first proposes a framework that identifies different audiences of computing and related computational thinking practices at the intersection of computer science and engineering. Then, based on a survey with 37 experts from industry and academia, this paper also suggests a series of modeling and simulation practices, methods, and tools for such audiences. Finally, this paper also reports experts' identified challenges and opportunities for integrating modeling and simulation practices at the undergraduate level. (C) 2016 Wiley Periodicals, Inc.
5th row	Much can be learned from the vast work on the use of computer simulations for inquiry learning for the integration of modeling and simulation practices in engineering education. This special issue presents six manuscripts that take steps toward evidence-based teaching and learning practices. These six studies present learning designs that align learning objectives, with evidence of the learning, and pedagogy. Here we highlight the main contributions from each paper individually, but also themes identified across all of them. These themes include (a) approaches for modeling-and-simulation-centric course design; (b) teaching practices and pedagogies for modeling and simulation implementation; and (c) evidence of learning with and about modeling and simulation practices. We conclude our introduction by highlighting desirable characteristics of studies that report on the effectiveness of modeling and simulation in engineering education, and with that we provide some recommendations for improving the scholarship of teaching and learning in this field.

Value	Count	Frequency (%)
the	42091	5.5%
and	30892	4.1%
of	26717	3.5%
in	20710	2.7%
to	20458	2.7%
a	14692	1.9%
students	8732	1.1%
for	8334	1.1%
thinking	7633	1.0%
that	7473	1.0%
Other values (22029)	573214	75.3%

Most occurring characters

Value	Count	Frequency (%)
	756845	14.2%
e	516582	9.7%
t	399701	7.5%
i	370547	6.9%
n	353731	6.6%
a	337460	6.3%
o	313576	5.9%
s	297583	5.6%
r	261360	4.9%
c	189019	3.5%
Other values (82)	1551842	29.0%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	5348246	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	756845	14.2%
e	516582	9.7%
t	399701	7.5%
i	370547	6.9%
n	353731	6.6%
a	337460	6.3%
o	313576	5.9%
s	297583	5.6%
r	261360	4.9%
c	189019	3.5%
Other values (82)	1551842	29.0%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	5348246	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	756845	14.2%
e	516582	9.7%
t	399701	7.5%
i	370547	6.9%
n	353731	6.6%
a	337460	6.3%
o	313576	5.9%
s	297583	5.6%
r	261360	4.9%
c	189019	3.5%
Other values (82)	1551842	29.0%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	5348246	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	756845	14.2%
e	516582	9.7%
t	399701	7.5%
i	370547	6.9%
n	353731	6.6%
a	337460	6.3%
o	313576	5.9%
s	297583	5.6%
r	261360	4.9%
c	189019	3.5%
Other values (82)	1551842	29.0%

times_cited_wos_core
Real number (ℝ)

High correlation Missing Skewed Zeros

Distinct	156
Distinct (%)	3.7%
Missing	6639
Missing (%)	61.3%
Infinite	0
Infinite (%)	0.0%
Mean	13.628101

Minimum	0
Maximum	3737
Zeros	1047
Zeros (%)	9.7%
Negative	0
Negative (%)	0.0%
Memory size	84.7 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	1
median	3
Q3	11
95-th percentile	50
Maximum	3737
Range	3737
Interquartile range (IQR)	10

Descriptive statistics

Standard deviation	72.140259
Coefficient of variation (CV)	5.2934931
Kurtosis	1738.7809
Mean	13.628101
Median Absolute Deviation (MAD)	3
Skewness	36.206914
Sum	57129
Variance	5204.2169
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0	1047	9.7%
1	516	4.8%
2	358	3.3%
3	267	2.5%
4	189	1.7%
5	188	1.7%
6	152	1.4%
7	134	1.2%
8	96	0.9%
9	95	0.9%
Other values (146)	1150	10.6%
(Missing)	6639	61.3%

Value	Count	Frequency (%)
0	1047	9.7%
1	516	4.8%
2	358	3.3%
3	267	2.5%
4	189	1.7%
5	188	1.7%
6	152	1.4%
7	134	1.2%
8	96	0.9%
9	95	0.9%

Value	Count	Frequency (%)
3737	1	< 0.1%
1337	1	< 0.1%
969	1	< 0.1%
816	1	< 0.1%
779	1	< 0.1%
752	1	< 0.1%
545	1	< 0.1%
484	1	< 0.1%
455	1	< 0.1%
384	1	< 0.1%

times_cited_wos_all
Real number (ℝ)

High correlation Missing Skewed Zeros

Distinct	184
Distinct (%)	4.4%
Missing	6639
Missing (%)	61.3%
Infinite	0
Infinite (%)	0.0%
Mean	17.158874

Minimum	0
Maximum	4918
Zeros	903
Zeros (%)	8.3%
Negative	0
Negative (%)	0.0%
Memory size	84.7 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	1
median	4
Q3	13
95-th percentile	62
Maximum	4918
Range	4918
Interquartile range (IQR)	12

Descriptive statistics

Standard deviation	94.431209
Coefficient of variation (CV)	5.5033453
Kurtosis	1777.9325
Mean	17.158874
Median Absolute Deviation (MAD)	4
Skewness	36.780789
Sum	71930
Variance	8917.2532
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0	903	8.3%
1	510	4.7%
2	348	3.2%
3	238	2.2%
4	204	1.9%
5	164	1.5%
6	150	1.4%
7	138	1.3%
9	100	0.9%
8	100	0.9%
Other values (174)	1337	12.3%
(Missing)	6639	61.3%

Value	Count	Frequency (%)
0	903	8.3%
1	510	4.7%
2	348	3.2%
3	238	2.2%
4	204	1.9%
5	164	1.5%
6	150	1.4%
7	138	1.3%
8	100	0.9%
9	100	0.9%

Value	Count	Frequency (%)
4918	1	< 0.1%
1766	1	< 0.1%
1314	1	< 0.1%
1081	1	< 0.1%
994	1	< 0.1%
918	1	< 0.1%
728	1	< 0.1%
601	1	< 0.1%
545	1	< 0.1%
472	1	< 0.1%

cited_reference_count_wos
Real number (ℝ)

High correlation Missing

Distinct	179
Distinct (%)	4.3%
Missing	6639
Missing (%)	61.3%
Infinite	0
Infinite (%)	0.0%
Mean	46.835639

Minimum	0
Maximum	678
Zeros	62
Zeros (%)	0.6%
Negative	0
Negative (%)	0.0%
Memory size	84.7 KiB

Quantile statistics

Minimum	0
5-th percentile	6
Q1	22
median	41.5
Q3	64
95-th percentile	104
Maximum	678
Range	678
Interquartile range (IQR)	42

Descriptive statistics

Standard deviation	33.769355
Coefficient of variation (CV)	0.72101833
Kurtosis	30.96061
Mean	46.835639
Median Absolute Deviation (MAD)	20.5
Skewness	2.5960496
Sum	196335
Variance	1140.3693
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
33	72	0.7%
31	70	0.6%
15	64	0.6%
39	63	0.6%
21	62	0.6%
0	62	0.6%
29	60	0.6%
28	59	0.5%
18	57	0.5%
58	57	0.5%
Other values (169)	3566	32.9%
(Missing)	6639	61.3%

Value	Count	Frequency (%)
0	62	0.6%
1	22	0.2%
2	12	0.1%
3	34	0.3%
4	30	0.3%
5	45	0.4%
6	45	0.4%
7	47	0.4%
8	44	0.4%
9	36	0.3%

Value	Count	Frequency (%)
678	1	< 0.1%
310	1	< 0.1%
217	1	< 0.1%
208	2	< 0.1%
207	1	< 0.1%
203	1	< 0.1%
201	1	< 0.1%
191	1	< 0.1%
190	1	< 0.1%
189	1	< 0.1%

wos_categories
Text

Missing

Distinct	396
Distinct (%)	9.5%
Missing	6654
Missing (%)	61.4%
Memory size	661.8 KiB

Length

Max length	258
Median length	187
Mean length	62.231506
Min length	3

Characters and Unicode

Total characters	259941
Distinct characters	47
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	192 ?
Unique (%)	4.6%

Sample

1st row	Psychology, Experimental
2nd row	Psychology, Experimental
3rd row	Biochemistry & Molecular Biology; Education, Scientific Disciplines
4th row	Computer Science, Interdisciplinary Applications; Education, Scientific Disciplines; Engineering, Multidisciplinary
5th row	Computer Science, Interdisciplinary Applications; Education, Scientific Disciplines; Engineering, Multidisciplinary

Value	Count	Frequency (%)
education	3403	12.6%
	3293	12.2%
science	2772	10.2%
computer	2611	9.7%
educational	2285	8.4%
research	2246	8.3%
scientific	1155	4.3%
disciplines	1155	4.3%
interdisciplinary	976	3.6%
applications	928	3.4%
Other values (142)	6227	23.0%

Most occurring characters

Value	Count	Frequency (%)
i	26571	10.2%
	22874	8.8%
e	22226	8.6%
c	21990	8.5%
n	18806	7.2%
t	15396	5.9%
a	14629	5.6%
o	13188	5.1%
r	10659	4.1%
s	9521	3.7%
Other values (37)	84081	32.3%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	259941	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
i	26571	10.2%
	22874	8.8%
e	22226	8.6%
c	21990	8.5%
n	18806	7.2%
t	15396	5.9%
a	14629	5.6%
o	13188	5.1%
r	10659	4.1%
s	9521	3.7%
Other values (37)	84081	32.3%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	259941	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
i	26571	10.2%
	22874	8.8%
e	22226	8.6%
c	21990	8.5%
n	18806	7.2%
t	15396	5.9%
a	14629	5.6%
o	13188	5.1%
r	10659	4.1%
s	9521	3.7%
Other values (37)	84081	32.3%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	259941	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
i	26571	10.2%
	22874	8.8%
e	22226	8.6%
c	21990	8.5%
n	18806	7.2%
t	15396	5.9%
a	14629	5.6%
o	13188	5.1%
r	10659	4.1%
s	9521	3.7%
Other values (37)	84081	32.3%

research_areas
Text

Missing

Distinct	187
Distinct (%)	4.5%
Missing	6654
Missing (%)	61.4%
Memory size	554.3 KiB

Length

Max length	140
Median length	139
Mean length	35.889155
Min length	3

Characters and Unicode

Total characters	149909
Distinct characters	45
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	108 ?
Unique (%)	2.6%

Sample

1st row	Psychology
2nd row	Psychology
3rd row	Biochemistry & Molecular Biology; Education & Educational Research
4th row	Computer Science; Education & Educational Research; Engineering
5th row	Computer Science; Education & Educational Research; Engineering

Value	Count	Frequency (%)
	3431	18.8%
research	2966	16.3%
education	2961	16.3%
educational	2961	16.3%
science	1916	10.5%
computer	1721	9.4%
engineering	426	2.3%
psychology	185	1.0%
other	157	0.9%
topics	157	0.9%
Other values (109)	1339	7.3%

Most occurring characters

Value	Count	Frequency (%)
c	14075	9.4%
	14043	9.4%
e	13494	9.0%
a	12524	8.4%
n	10005	6.7%
i	9999	6.7%
o	9433	6.3%
t	8545	5.7%
u	7975	5.3%
E	6486	4.3%
Other values (35)	43330	28.9%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	149909	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
c	14075	9.4%
	14043	9.4%
e	13494	9.0%
a	12524	8.4%
n	10005	6.7%
i	9999	6.7%
o	9433	6.3%
t	8545	5.7%
u	7975	5.3%
E	6486	4.3%
Other values (35)	43330	28.9%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	149909	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
c	14075	9.4%
	14043	9.4%
e	13494	9.0%
a	12524	8.4%
n	10005	6.7%
i	9999	6.7%
o	9433	6.3%
t	8545	5.7%
u	7975	5.3%
E	6486	4.3%
Other values (35)	43330	28.9%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	149909	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
c	14075	9.4%
	14043	9.4%
e	13494	9.0%
a	12524	8.4%
n	10005	6.7%
i	9999	6.7%
o	9433	6.3%
t	8545	5.7%
u	7975	5.3%
E	6486	4.3%
Other values (35)	43330	28.9%

publisher_wos
Text

Missing

Distinct	255
Distinct (%)	6.1%
Missing	6639
Missing (%)	61.3%
Memory size	495.6 KiB

Length

Max length	74
Median length	71
Mean length	21.341842
Min length	4

Characters and Unicode

Total characters	89465
Distinct characters	56
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	129 ?
Unique (%)	3.1%

Sample

1st row	WILEY
2nd row	WILEY
3rd row	WILEY
4th row	WILEY
5th row	WILEY

Value	Count	Frequency (%)
springer	896	7.4%
assoc	867	7.2%
computing	811	6.7%
machinery	811	6.7%
ltd	639	5.3%
	506	4.2%
ieee	431	3.6%
publishing	367	3.0%
ag	365	3.0%
francis	338	2.8%
Other values (478)	6076	50.2%

Most occurring characters

Value	Count	Frequency (%)
I	8783	9.8%
E	8296	9.3%
	7915	8.8%
N	7179	8.0%
S	6163	6.9%
R	6139	6.9%
A	5907	6.6%
C	5185	5.8%
O	4522	5.1%
T	4323	4.8%
Other values (46)	25053	28.0%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	89465	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
I	8783	9.8%
E	8296	9.3%
	7915	8.8%
N	7179	8.0%
S	6163	6.9%
R	6139	6.9%
A	5907	6.6%
C	5185	5.8%
O	4522	5.1%
T	4323	4.8%
Other values (46)	25053	28.0%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	89465	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
I	8783	9.8%
E	8296	9.3%
	7915	8.8%
N	7179	8.0%
S	6163	6.9%
R	6139	6.9%
A	5907	6.6%
C	5185	5.8%
O	4522	5.1%
T	4323	4.8%
Other values (46)	25053	28.0%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	89465	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
I	8783	9.8%
E	8296	9.3%
	7915	8.8%
N	7179	8.0%
S	6163	6.9%
R	6139	6.9%
A	5907	6.6%
C	5185	5.8%
O	4522	5.1%
T	4323	4.8%
Other values (46)	25053	28.0%

language_wos
Categorical

High correlation Imbalance Missing

Distinct	11
Distinct (%)	0.3%
Missing	6639
Missing (%)	61.3%
Memory size	592.5 KiB

English	4064
Spanish	86
Portuguese	25
French	4
German	3
Other values (6)	10

Length

Max length	11
Median length	7
Mean length	7.0171756
Min length	6

Characters and Unicode

Total characters	29416
Distinct characters	28
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	3 ?
Unique (%)	0.1%

Sample

1st row	English
2nd row	English
3rd row	English
4th row	English
5th row	English

Common Values

Value	Count	Frequency (%)
English	4064	37.5%
Spanish	86	0.8%
Portuguese	25	0.2%
French	4	< 0.1%
German	3	< 0.1%
Turkish	3	< 0.1%
Russian	2	< 0.1%
Korean	2	< 0.1%
Unspecified	1	< 0.1%
Chinese	1	< 0.1%
(Missing)	6639	61.3%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
english	4064	96.9%
spanish	86	2.1%
portuguese	25	0.6%
french	4	0.1%
german	3	0.1%
turkish	3	0.1%
russian	2	< 0.1%
korean	2	< 0.1%
unspecified	1	< 0.1%
chinese	1	< 0.1%

Most occurring characters

Value	Count	Frequency (%)
s	4184	14.2%
n	4165	14.2%
i	4160	14.1%
h	4158	14.1%
g	4089	13.9%
E	4064	13.8%
l	4064	13.8%
a	95	0.3%
p	87	0.3%
S	86	0.3%
Other values (18)	264	0.9%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	29416	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
s	4184	14.2%
n	4165	14.2%
i	4160	14.1%
h	4158	14.1%
g	4089	13.9%
E	4064	13.8%
l	4064	13.8%
a	95	0.3%
p	87	0.3%
S	86	0.3%
Other values (18)	264	0.9%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	29416	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
s	4184	14.2%
n	4165	14.2%
i	4160	14.1%
h	4158	14.1%
g	4089	13.9%
E	4064	13.8%
l	4064	13.8%
a	95	0.3%
p	87	0.3%
S	86	0.3%
Other values (18)	264	0.9%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	29416	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
s	4184	14.2%
n	4165	14.2%
i	4160	14.1%
h	4158	14.1%
g	4089	13.9%
E	4064	13.8%
l	4064	13.8%
a	95	0.3%
p	87	0.3%
S	86	0.3%
Other values (18)	264	0.9%

affiliations_wos
Text

Missing

Distinct	2580
Distinct (%)	64.8%
Missing	6848
Missing (%)	63.2%
Memory size	669.9 KiB

Length

Max length	725
Median length	266
Mean length	68.173989
Min length	5

Characters and Unicode

Total characters	271537
Distinct characters	67
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	2014 ?
Unique (%)	50.6%

Sample

1st row	Ruhr University Bochum; Ruhr University Bochum
2nd row	University of Alberta; University of Alberta
3rd row	University of Sydney; University of New South Wales Sydney; University of Sydney; University of Sydney
4th row	Purdue University System; Purdue University; Purdue University System; Purdue University; Instituto Federal da Bahia (IFBA)
5th row	Purdue University System; Purdue University; University of Twente

Value	Count	Frequency (%)
university	6809	19.8%
of	3864	11.2%
system	1013	2.9%
de	866	2.5%
state	732	2.1%
universidad	557	1.6%
technology	500	1.5%
national	465	1.3%
	418	1.2%
normal	390	1.1%
Other values (2089)	18839	54.7%

Most occurring characters

Value	Count	Frequency (%)
	30470	11.2%
i	26367	9.7%
e	21546	7.9%
n	19635	7.2%
a	16541	6.1%
t	16513	6.1%
r	14510	5.3%
o	14251	5.2%
s	13993	5.2%
y	9654	3.6%
Other values (57)	88057	32.4%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	271537	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	30470	11.2%
i	26367	9.7%
e	21546	7.9%
n	19635	7.2%
a	16541	6.1%
t	16513	6.1%
r	14510	5.3%
o	14251	5.2%
s	13993	5.2%
y	9654	3.6%
Other values (57)	88057	32.4%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	271537	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	30470	11.2%
i	26367	9.7%
e	21546	7.9%
n	19635	7.2%
a	16541	6.1%
t	16513	6.1%
r	14510	5.3%
o	14251	5.2%
s	13993	5.2%
y	9654	3.6%
Other values (57)	88057	32.4%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	271537	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	30470	11.2%
i	26367	9.7%
e	21546	7.9%
n	19635	7.2%
a	16541	6.1%
t	16513	6.1%
r	14510	5.3%
o	14251	5.2%
s	13993	5.2%
y	9654	3.6%
Other values (57)	88057	32.4%

addresses_wos
Text

Missing

Distinct	4120
Distinct (%)	98.5%
Missing	6648
Missing (%)	61.4%
Memory size	1.2 MiB

Length

Max length	1245
Median length	468
Mean length	201.46761
Min length	38

Characters and Unicode

Total characters	842739
Distinct characters	74
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	4065 ?
Unique (%)	97.2%

Sample

1st row	[Bordewieck, Martin; Elson, Malte] Ruhr Univ Bochum, Fac Psychol, Bochum, Germany; [Bordewieck, Martin; Elson, Malte] Ruhr Univ Bochum, Horst Gortz Inst IT Secur, Bochum, Germany
2nd row	[Pan, Zexuan; Cui, Ying; Leighton, Jacqueline P.; Cutumisu, Maria] Univ Alberta, Fac Educ, Ctr Res Appl Measurement & Evaluat, Dept Educ Psychol, Edmonton, AB, Canada; [Cutumisu, Maria] Univ Alberta, Fac Educ, Dept Educ Psychol, 6-102 Educ North, Edmonton, AB T6G 2G5, Canada
3rd row	[Gough, Phillip] Univ Sydney, Affect Interact Lab, Sch Architecture Design & Planning, Camperdown, NSW, Australia; [Bown, Oliver] Univ New South Wales, Fac Art & Design, Kensington, NSW, Australia; [Campbell, Craig R.; Poronnik, Philip] Univ Sydney, Fac Med & Hlth, Sch Med Sci, FMH Media Lab,Educ Innovat, Camperdown, NSW, Australia; [Ross, Pauline M.] Univ Sydney, Fac Sci, Sch Life & Environm Sci, Camperdown, NSW 2006, Australia
4th row	[Magana, Alejandra J.] Purdue Univ, Comp & Informat Technol & Engn Educ, W Lafayette, IN 47906 USA; [Coutinho, Genisson Silva] Purdue Univ, Engn Educ, W Lafayette, IN 47906 USA; [Coutinho, Genisson Silva] Inst Fed Educ Ciencia & Tecnol Bahia, Mech & Mat Technol Dept, Salvador, BA, Brazil
5th row	[Magana, Alejandra J.] Purdue Univ, Dept Comp & Informat Technol, Knoy Hall Bldg,Room 231,401 N Grant St, W Lafayette, IN 47907 USA; [de Jong, Ton] Univ Twente, Fac Behav Management & Social Sci, Enschede, Netherlands

Value	Count	Frequency (%)
univ	7279	5.9%
educ	3066	2.5%
	2792	2.3%
dept	2583	2.1%
usa	2486	2.0%
sci	1859	1.5%
technol	1333	1.1%
sch	1197	1.0%
china	1158	0.9%
comp	1137	0.9%
Other values (19011)	97633	79.7%

Most occurring characters

Value	Count	Frequency (%)
	118340	14.0%
a	62138	7.4%
n	53955	6.4%
i	48233	5.7%
e	46325	5.5%
,	43539	5.2%
o	34533	4.1%
r	30196	3.6%
l	26567	3.2%
t	25192	3.0%
Other values (64)	353721	42.0%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	842739	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	118340	14.0%
a	62138	7.4%
n	53955	6.4%
i	48233	5.7%
e	46325	5.5%
,	43539	5.2%
o	34533	4.1%
r	30196	3.6%
l	26567	3.2%
t	25192	3.0%
Other values (64)	353721	42.0%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	842739	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	118340	14.0%
a	62138	7.4%
n	53955	6.4%
i	48233	5.7%
e	46325	5.5%
,	43539	5.2%
o	34533	4.1%
r	30196	3.6%
l	26567	3.2%
t	25192	3.0%
Other values (64)	353721	42.0%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	842739	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	118340	14.0%
a	62138	7.4%
n	53955	6.4%
i	48233	5.7%
e	46325	5.5%
,	43539	5.2%
o	34533	4.1%
r	30196	3.6%
l	26567	3.2%
t	25192	3.0%
Other values (64)	353721	42.0%

source_wos
Boolean

Constant Missing

Distinct	1
Distinct (%)	< 0.1%
Missing	6639
Missing (%)	61.3%
Memory size	355.0 KiB

True	4192
(Missing)	6639

Value	Count	Frequency (%)
True	4192	38.7%
(Missing)	6639	61.3%

Interactions

Correlations

	cited_by	cited_by_scopus	cited_reference_count_wos	document_type	document_type_wos	has_scopus	has_wos	language_scopus	language_wos	publication_type_wos	times_cited_wos_all	times_cited_wos_core	year	year_wos
cited_by	1.000	1.000	0.254	0.000	0.146	0.000	0.000	0.000	0.000	0.000	0.965	0.960	-0.436	-0.468
cited_by_scopus	1.000	1.000	0.250	0.035	0.171	1.000	0.000	0.000	0.000	0.000	0.960	0.959	-0.438	-0.486
cited_reference_count_wos	0.254	0.250	1.000	0.481	0.486	0.024	1.000	0.000	0.000	0.241	0.278	0.267	0.383	0.384
document_type	0.000	0.035	0.481	1.000	0.900	0.622	0.328	0.013	0.043	0.831	0.000	0.000	0.073	0.168
document_type_wos	0.146	0.171	0.486	0.900	1.000	0.269	1.000	0.000	0.000	0.845	0.111	0.105	0.214	0.192
has_scopus	0.000	1.000	0.024	0.622	0.269	1.000	0.336	1.000	0.158	0.255	0.000	0.000	0.101	0.000
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times_cited_wos_all	0.965	0.960	0.278	0.000	0.111	0.000	1.000	0.000	0.000	0.000	1.000	0.984	-0.442	-0.442
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Missing values

A simple visualization of nullity by column.

Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

	doi	title	year	journal	document_type	cited_by	has_scopus	has_wos	authors	author_keywords	index_keywords	abstract	cited_by_scopus	references_count_scopus	publisher_scopus	language_scopus	affiliations_scopus	country_scopus	source_scopus	title_wos	year_wos	journal_wos	document_type_wos	publication_type_wos	authors_wos	author_full_names_wos	author_keywords_wos	keywords_plus_wos	abstract_wos	times_cited_wos_core	times_cited_wos_all	cited_reference_count_wos	wos_categories	research_areas	publisher_wos	language_wos	affiliations_wos	addresses_wos	source_wos
0	10.1002/(sici)1096-9128(199601)8:1<47::aid-cpe194>3.0.co;2-9	Benchmarking the computation and communication performance of the CM-5	1996	Concurrency Practice and Experience	Article	2.0	True	False	Dinçer, K.; Bozkus, Z.; Ranka, S.; Fox, G.	NaN	Bandwidth; Calculations; Computational methods; Computer networks; Data communication systems; Distributed computer systems; Mathematical models; Parallel algorithms; Performance; Standards; Synchronization; Topology; Benchmarking; Communication latency; Communication start-up time; Computational processing rate; Control network; Diagnostic network; Gaussian elimination code; Global communication; Point to point communication; Vectorization; Parallel processing systems	Thinking Machines' CM-5 machine is a distributed-memory, message-passing computer. In the paper we devise a performance benchmark for the base and vector units and the data communication networks of the CM-5 machine. We model the communication characteristics such as communication latency and bandwidths of point-to-point and global communication primitives. We show, on a simple Gaussian elimination code, that an accurate static performance estimation of parallel algorithms is possible by using those basic machine properties connected with computation, vectorization, communication and synchronization. Furthermore, we describe the embedding of meshes or hypercubes on the CM-5 fat-tree topology and illustrate the performance results of their basic communication primitives.	2.0	Solving Problems on Concurrent Processors, (1988); Hypercube Algorithms with Applications to Image Processing and Pattern Recognition, (1990); Bomans, Luc, Benchmarking the iPSC/2 hypercube multiprocessor, Concurrency Practice and Experience, 1, 1, pp. 3-18, (1989); Proceedings of the Frontiers of Massively Parallel Computation, (1992); Hockney, Roger W., Performance parameters and benchmarking of supercomputers, Parallel Computing, 17, 10-11, pp. 1111-1130, (1991); Kwan, Thomas T., Communication and computation performance of the CM-5, pp. 192-201, (1993); Leiserson, Charles E., Network architecture of the Connection Machine CM-5, pp. 272-285, (1992); Lin, Mengjou, Performance evaluation of the CM-5 interconnection network, pp. 189-198, (1993); Ponnusamy, Ravi, Experimental performance evaluation of the CM-5, Journal of Parallel and Distributed Computing, 19, 3, pp. 192-202, (1993); Bailey, David H., The nas parallel benchmarks, International Journal of High Performance Computing Applications, 5, 3, pp. 63-73, (1991)	John Wiley and Sons Ltd	English	Northeast Parallel Architectures Center, Syracuse University, Syracuse, NY, United States	NaN	True	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1	10.1002/(sici)1097-0193(1999)8:2/3<128::aid-hbm10>3.0.co;2-g	Computational modeling of high-level cognition and brain function	1999	Human Brain Mapping	Conference paper	73.0	True	False	Just, M.A.; Carpenter, P.A.; Varma, S.	4CAPS; Brain function; PET; T-MRI	brain function; cognition; computer model; computer simulation; conference paper; image processing; imaging system; nuclear magnetic resonance imaging; priority journal; Brain; Cognition; Humans; Magnetic Resonance Imaging; Models, Neurological; Neural Networks (Computer); Thinking	This article describes a computational modeling architecture, 4CAPS, which is consistent with key properties of cortical function and makes good contact with functional neuroimaging results. Like earlier cognitive models such as SOAR, ACT-R, 3CAPS, and EPIC, the proposed cognitive model is implemented in a computer simulation that predicts observable variables such as human response times and error patterns. In addition, the proposed 4CAPS model accounts for the functional decomposition of the cognitive system and predicts fMRI activation levels and their localization within specific cortical regions, by incorporating key properties of cortical function into the design of the modeling system.	73.0	Rules of the Mind, (1993); Awh, Edward, Dissociation of Storage and Rehearsal in Verbal Working Memory: Evidence from Positron Emission Tomography, Psychological Science, 7, 1, pp. 25-31, (1996); Agrammatism, (1985); Carpenter, Patricia Ann, Graded functional activation in the visuospatial system with the amount of task demand, Journal of Cognitive Neuroscience, 11, 1, pp. 9-24, (1999); Carpenter, Patricia Ann, What one intelligence test measures: A theoretical account of the processing in the Raven progressive matrices test, Psychological Review, 97, 3, pp. 404-431, (1990); Collins, Allan M., Retrieval time from semantic memory, Journal of Verbal Learning and Verbal Behavior, 8, 2, pp. 240-247, (1969); Rethinking Innateness A Connectionist Perspective on Development, (1996); Human Brain Function, (1997); Gabrieli, John D.E., The role of left prefrontal cortex in language and memory, Proceedings of the National Academy of Sciences of the United States of America, 95, 3, pp. 906-913, (1998); Grafman, Jordan Henry, Similarities and Distinctions among Current Models of Prefrontal Cortical Functions, Annals of the New York Academy of Sciences, 769, 1, pp. 337-368, (1995)	NaN	English	Center for Cognitive Brain Imaging, Carnegie Mellon University, Pittsburgh, PA, United States; Department of Psychology, Carnegie Mellon University, Pittsburgh, PA, United States	NaN	True	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2	10.1002/(sici)1097-0363(199706)24:12<1321::aid-fld562>3.0.co;2-l	Parallel computation of incompressible flows with complex geometries	1997	International Journal for Numerical Methods in Fluids	Article	100.0	True	False	Johnson, A.A.; Tezduyar, T.	Automobile; Complex geometries; Mesh generation; Parallel flow simulation	Aerodynamics; Automobiles; Computer simulation; Finite element method; Flow interactions; Navier Stokes equations; Parallel processing systems; Incompressible flow; Computational fluid dynamics; air flow; incompressible flow; Navier-Stokes equations; vehicles	We present our numerical methods for the solution of large-scale incompressible flow applications with complex geometries. These methods include a stabilized finite element formulation of the Navier-Stokes equations, implementation of this formulation on parallel architectures such as the Thinking Machines CM-5 and the CRAY T3D, and automatic 3D mesh generation techniques based on Delaunay-Voronoï methods for the discretization of complex domains. All three of these methods are required for the numerical simulation of most engineering applications involving fluid flow. We apply these methods to the simulation of airflow past an automobile and fluid-particle interactions. The simulation of airflow past an automobile is of very large scale with a high level of detail and yielded many interesting airflow patterns which help in understanding the aerodynamic characteristics of such vehicles. © 1997 by John Wiley & Sons, Ltd.	100.0	Tezduyar, Tayfun E., Computation of unsteady incompressible flows with the stabilized finite element methods: Space-time formulations, iterative strategies and massively parallel implementations, American Society of Mechanical Engineers, Pressure Vessels and Piping Division (Publication) PVP, 246, pp. 7-24, (1992); Añón, J. C R, Computation of incompressible flows with implicit finite element implementations on the Connection Machine, Computer Methods in Applied Mechanics and Engineering, 108, 1-2, pp. 99-118, (1993); Tezduyar, Tayfun E., Parallel Finite-Element Computation of 3D Flows, Computer, 26, 10, pp. 27-36, (1993); Computational Mechanics 95 Proc Int Conf on Computational Engineering Science, (1995); Pvm Parallel Virtual Machine, (1994); Añón, J. C R, An efficient communications strategy for finite element methods on the Connection Machine CM-5 system, Computer Methods in Applied Mechanics and Engineering, 113, 3-4, pp. 363-387, (1994); Mesh Generation and Update Strategies for Parallel Computation of Flow Problems with Moving Boundaries and Interfaces, (1995); Technical Report, (1995); Aliabadi, Shabrouz K., Parallel fluid dynamics computations in aerospace applications, International Journal for Numerical Methods in Fluids, 21, 10, pp. 783-805, (1995); Tezduyar, Tayfun E., Massively parallel finite element simulation of compressible and incompressible flows, Computer Methods in Applied Mechanics and Engineering, 119, 1-2, pp. 157-177, (1994)	John Wiley and Sons Ltd	English	Army HPC Research Center, College of Science and Engineering, Minneapolis, MN, United States	NaN	True	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
3	10.1002/(sici)1097-0363(199706)24:12<1353::aid-fld564>3.0.co;2-6	Parallel finite element simulation of large ram-air parachutes	1997	International Journal for Numerical Methods in Fluids	Article	40.0	True	False	Kalro, V.; Aliabadi, S.; Garrard, W.; Tezduyar, T.; Mittal, S.; Stein, K.	3D flow simulations; Parachutes; Parallel computations	Computational fluid dynamics; Computer simulation; Drag; Finite element method; Lift; Mathematical models; Navier Stokes equations; Newtonian flow; Parallel processing systems; Three dimensional computer graphics; Canopy inflation; Ram air parachutes; Parachutes; computer simulation; finite element method; parachutes	In the near future, large ram-air parachutes are expected to provide the capability of delivering 21 ton pay loads from altitudes as high as 25,000 ft. In development and test and evaluation of these parachutes the size of the parachute needed and the deployment stages involved make high-performance computing (HPC) simulations a desirable alternative to costly airdrop tests. Although computational simulations based on realistic, 3D, time-dependent models will continue to be a major computational challenge, advanced finite element simulation techniques recently developed for this purpose and the execution of these techniques on HPC platforms are significant steps in the direction to meet this challenge. In this paper, two approaches for analysis of the inflation and gliding of ram-air parachutes are presented. In one of the approaches the point mass flight mechanics equations are solved with the time-varying drag and lift areas obtained from empirical data. This approach is limited to parachutes with similar configurations to those for which data are available. The other approach is 3D finite element computations based on the Navier-Stokes equations governing the airflow around the parachute canopy and Newton's law of motion governing the 3D dynamics of the canopy, with the forces acting on the canopy calculated from the simulated flow field. At the earlier stages of canopy inflation the parachute is modelled as an expanding box, whereas at the later stages, as it expands, the box transforms to a parafoil and glides. These finite element computations are carried out on the massively parallel supercomputers CRAY T3D and Thinking Machines CM-5, typically with millions of coupled, non-linear finite element equations solved simultaneously at every time step or pseudo-time step of the simulation. © 1997 by John Wiley & Sons, Ltd.	40.0	Añón, J. C R, Development testing of large ram air inflated wings, (1993); Garrard, William L., Inflation analysis of ram air inflated gliding parachutes, pp. 186-198, (1995); Aliabadi, Shahrouz K., Parallel finite element computation of the dynamics of large ram air parachutes, pp. 278-293, (1995); Añón, J. C R, SEMI-EMPIRICAL THEORY TO PREDICT THE LOAD-TIME HISTORY OF AN INFLATING PARACHUTE., pp. 177-185, (1984); University of Minnesota Parachute Systems Technology Short Courses, (1994); AIAA Paper 91 0862, (1991); Tezduyar, Tayfun E., Parallel Finite-Element Computation of 3D Flows, Computer, 26, 10, pp. 27-36, (1993); Tezduyar, Tayfun E., Massively parallel finite element simulation of compressible and incompressible flows, Computer Methods in Applied Mechanics and Engineering, 119, 1-2, pp. 157-177, (1994); Tezduyar, Tayfun E., A new strategy for finite element computations involving moving boundaries and interfaces-The deforming-spatial-domain/space-time procedure: I. The concept and the preliminary numerical tests, Computer Methods in Applied Mechanics and Engineering, 94, 3, pp. 339-351, (1992); Tezduyar, Tayfun E., A new strategy for finite element computations involving moving boundaries and interfaces-The deforming-spatial-domain/space-time procedure: II. Computation of free-surface flows, two-liquid flows, and flows with drifting cylinders, Computer Methods in Applied Mechanics and Engineering, 94, 3, pp. 353-371, (1992)	John Wiley and Sons Ltd	English	Army HPC Research Center, College of Science and Engineering, Minneapolis, MN, United States; Indian Institute of Technology Kanpur, Kanpur, UP, India; U.S. Army Natick RD and E Center, Natick, MA, United States	NaN	True	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
4	10.1002/(sici)1097-0363(199706)24:12<1371::aid-fld565>3.0.co;2-7	Parallel finite element methods for large-scale computation of storm surges and tidal flows	1997	International Journal for Numerical Methods in Fluids	Article	24.0	True	False	Kashiyama, K.; Saitoh, K.; Behr, M.; Tezduyar, T.	Implicit space-time formulation; Parallel finite element method; Storm surge; Three-step explicit formulation; Tidal flow	Computational fluid dynamics; Computer simulation; Finite element method; Parallel processing systems; Tides; Tidal flows; Unstructured grid formulations; Storms; computer simulation; finite element method; storms; tidal flows	Massively parallel finite element methods for large-scale computation of storm surges and tidal flows are discussed here. The finite element computations, carried out using unstructured grids, are based on a three-step explicit formulation and on an implicit space-time formulation. Parallel implementations of these unstructured grid-based formulations are carried out on the Fujitsu Highly Parallel Computer AP1000 and on the Thinking Machines CM-5. Simulations of the storm surge accompanying the Ise-Bay typhoon in 1959 and of the tidal flow in Tokyo Bay serve as numerical examples. The impact of parallelization on this type of simulation is also investigated. The present methods are shown to be useful and powerful tools for the analysis of storm surges and tidal flows. © 1997 by John Wiley & Sons, Ltd.	24.0	Añón, J. C R, Tide and storm surge predictions using finite element model, Journal of Hydraulic Engineering, 118, 10, pp. 1373-1390, (1992); Añón, J. C R, Massively parallel finite element method for large scale computation of storm surge, 2, pp. 79-86, (1996); Añón, J. C R, Three‐step explicit finite element computation of shallow water flows on a massively parallel computer, International Journal for Numerical Methods in Fluids, 21, 10, pp. 885-900, (1995); Añón, J. C R, Selective lumping finite element method for shallow water flow, International Journal for Numerical Methods in Fluids, 2, 1, pp. 89-112, (1982); Tezduyar, Tayfun E., Computation of unsteady incompressible flows with the stabilized finite element methods: Space-time formulations, iterative strategies and massively parallel implementations, American Society of Mechanical Engineers, Pressure Vessels and Piping Division (Publication) PVP, 246, pp. 7-24, (1992); Hughes, Thomas J.R., A multi-dimensioal upwind scheme with no crosswind diffusion., 34 )., pp. 19-35, (1979); Tezduyar, Tayfun E., FINITE ELEMENT FORMULATIONS FOR CONVECTION DOMINATED FLOWS WITH PARTICULAR EMPHASIS ON THE COMPRESSIBLE EULER EQUATIONS., (1983); Añón, J. C R, Finite element computation of compressible flows with the SUPG formulation, American Society of Mechanical Engineers, Fluids Engineering Division (Publication) FEDSM, 123, pp. 21-27, (1991); Añón, J. C R, SUPG finite element computation of compressible flows with the entropy and conservation variables formulations, Computer Methods in Applied Mechanics and Engineering, 104, 3, pp. 397-422, (1993); Aliabadi, Shabrouz K., SUPG finite element computation of viscous compressible flows based on the conservation and entropy variables formulations, Computational Mechanics, 11, 5-6, pp. 300-312, (1993)	John Wiley and Sons Ltd	English	Department of Civil Engineering, Chuo University, Hachioji, Tokyo, Japan; University of Minnesota Twin Cities, Minneapolis, MN, United States	NaN	True	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
5	10.1002/(sici)1097-0363(199706)24:12<1417::aid-fld567>3.0.co;2-n	Parallel finite element computation of missile aerodynamics	1997	International Journal for Numerical Methods in Fluids	Article	5.0	True	False	Sturek, W.B.; Ray, S.; Aliabadi, S.; Waters, C.; Tezduyar, T.	Compressible flows; Missile aerodynamics; Parallel computing methods	Algorithms; Compressible flow; Computational fluid dynamics; Finite element method; Mathematical models; Navier Stokes equations; Parallel processing systems; Reynolds number; Supersonic aerodynamics; Turbulence; Viscous flow; Thinking machines; Turbulence models; Missiles; aerodynamics; computational fluid dynamics; finite element method; missiles; Navier-Stokes equations	A flow simulation tool, developed by the authors at the Army HPC Research Center, for compressible flows governed by the Navier-Stokes equations is used to study missile aerodynamics at supersonic speeds, high angles of attack and for large Reynolds numbers. The goal of this study is the evaluation of this Navier-Stokes computational technique for the prediction of separated flow fields around high-length-to-diameter (L/D) bodies. In particular, this paper addresses two issues: (i) turbulence modelling with a finite element computational technique and (ii) efficient performance of the computational technique on two different multiprocessor mainframes, the Thinking Machines CM-5 and CRAY T3D. The paper first provides a discussion of the Navier-Stokes computational technique and the algorithm issues for achieving efficient performance on the CM-5 and T3D. Next, comparisons are shown between the computation and experiment for supersonic ramp flow to evaluate the suitability of the turbulence model. Following that, results of the computations for missile flow fields are shown for laminar and turbulent viscous effects. © 1997 by John Wiley & Sons, Ltd.	5.0	Parallel Finite Element Computations in Aerospace Applications, (1994); Advances in Numerical Simulation of Turbulent Flows, (1991); Añón, J. C R, The numerical computation of turbulent flows, Computer Methods in Applied Mechanics and Engineering, 3, 2, pp. 269-289, (1974); AIAA Paper 92 0670, (1983); A One Equation Turbulence Transport Model for High Reynolds Number Wall Bounded Flows, (1990); AIAA Paper 93 3099, (1993); Aliabadi, Shabrouz K., Parallel fluid dynamics computations in aerospace applications, International Journal for Numerical Methods in Fluids, 21, 10, pp. 783-805, (1995); Añón, J. C R, Implementation of implicit finite element methods for incompressible flows on the CM-5, Computer Methods in Applied Mechanics and Engineering, 119, 1-2, pp. 95-111, (1994); Computational Mechanics 95 Proc Int Conf on Computational Engineering Science, (1995); Solving Large Scale Problems in Mechanics Parallel and Distributed Computer Applications, (1997)	John Wiley and Sons Ltd	English	U.S. Army Research Laboratory, Adelphi, MD, United States; College of Science and Engineering, Minneapolis, MN, United States	NaN	True	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
6	10.1002/(sici)1097-0363(199706)24:12<1449::aid-fld569>3.0.co;2-8	Parallel finite element calculation of flow in a three-dimensional lid-driven cavity using the CM-5 and T3D	1997	International Journal for Numerical Methods in Fluids	Article	17.0	True	False	Yeckel, A.; Smith, J.W.; Derby, J.J.	Flow; Incompressible; Parallel finite element; Steady; Three-dimensional	Bifurcation (mathematics); Cavitation; Convergence of numerical methods; Finite element method; Parallel processing systems; Reynolds number; Lid driven cavities; Computational fluid dynamics; cavities; finite element method; steady flow	Steady flows in a three-dimensional lid-driven cavity at moderate Reynolds number are studied using various methods of parallel programming on the Cray T3D and Thinking Machines CM-5. These three-dimensional flows are compared with flows computed in a two-dimensional cavity. Solutions at Reynolds number up to 500 agree well with the experimental data of Aidun et al. (Phys. Fluids A, 3, 2081-2091 (1991)) for the location of separation of the secondary eddy at the downstream wall. Convergence of the three-dimensional problem using GMRES with diagonal preconditioning could not be obtained at Reynolds number greater than about 500. We speculate that the source of the difficulty is the loss of stability via pitchfork and Hopf bifurcations identified by Aidun et al. The relative performance of various methods of message passing on the Cray T3D is compared with the data-parallel mode of programming on the CM-5. No clear advantage between machines or message-passing methods is distinguished. © 1997 by John Wiley & Sons, Ltd.	17.0	Añón, J. C R, Theoretical Modeling of Czochralski Crystal Growth, MRS Bulletin, 13, 10, pp. 29-35, (1988); Science and Technology of Crystal Growth, (1995); Science and Technology of Crystal Growth, (1995); Añón, J. C R, Large-scale numerical analysis of materials processing systems: High-temperature crystal growth and molten glass flows, Computer Methods in Applied Mechanics and Engineering, 112, 1-4, pp. 69-89, (1994); Añón, J. C R, Massively parallel finite element computations of three-dimensional, time-dependent, incompressible flows in materials processing systems, Computer Methods in Applied Mechanics and Engineering, 119, 1-2, pp. 139-156, (1994); Añón, J. C R, Massively parallel finite element analysis of coupled, incompressible flows: A benchmark computation of baroclinic annulus waves, International Journal for Numerical Methods in Fluids, 21, 10, pp. 1007-1014, (1995); Añón, J. C R, Three-dimensional melt flows in Czochralski oxide growth: high-resolution, massively parallel, finite element computations, Journal of Crystal Growth, 152, 3, pp. 169-181, (1995); Añón, J. C R, On the effects of ampoule tilting during vertical Bridgman growth: Three-dimensional computations via a massively parallel, finite element method, Journal of Crystal Growth, 167, 1-2, pp. 292-304, (1996); Añón, J. C R, Analytical and numerical studies of the structure of steady separated flows, Journal of Fluid Mechanics, 24, 1, pp. 113-151, (1966); Añón, J. C R, High-Re solutions for incompressible flow using the Navier-Stokes equations and a multigrid method, Journal of Computational Physics, 48, 3, pp. 387-411, (1982)	John Wiley and Sons Ltd	English	College of Science and Engineering, Minneapolis, MN, United States; College of Science and Engineering, Minneapolis, MN, United States	NaN	True	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
7	10.1002/(sici)1098-111x(199702)12:2<105::aid-int1>3.0.co;2-u	A knowledge level analysis of taxonomic domains	1997	International Journal of Intelligent Systems	Article	5.0	True	False	Domingo, M.; Sierra, C.	NaN	Artificial intelligence; Computational methods; Computer software; Knowledge based systems; Knowledge representation; Programming theory; Domain models; Knowledge level theory; Knowledge engineering	The Knowledge Level (KL) is an abstract level of description, prior to the symbol or software level, which aims at discovering the components of expertise without thinking of computational aspects. The KL analysis emphasizes the regularities in knowledge use for knowledge engineering. We consider the knowledge level analysis the AI counterpart of the specification of programs. Then, it must be possible to define formal ways of putting in relation the KL analysis with computational elements that implement it. The ultimate goal of the research presented in this article is to contribute in the filling of the gap between specification at the KL and implementation. To do so we propose (i) a particular interpretation of the three main concepts involved in the knowledge level theories, i.e., tasks, methods, and domain models, and (ii) a mapping between these notions and computational elements of Milord II, a shell developed at the IIIA Institute and used as the target programming environment of an example in biological identification. © 1997 John Wiley & Sons, Inc.	5.0	Añón, J. C R, The knowledge level, Artificial Intelligence, 18, 1, pp. 87-127, (1982); Clancey, William J., Heuristic classification, Artificial Intelligence, 27, 3, pp. 289-350, (1985); Añón, J. C R, Design problem solving. A task analysis, AI Magazine, 11, 4, pp. 59-71, (1990); Añón, J. C R, Components of expertise, AI Magazine, 11, 2, pp. 28-49, (1990); Second Generation Expert Systems, (1993); Añón, J. C R, KADS: a modelling approach to knowledge engineering, Knowledge Acquisition, 4, 1, pp. 5-53, (1992); Cc AI, (1993); Qualitative Reasoning and Decision Technologies, (1993); Sponges in Time and Space, (1994); Añón, J. C R, Managing linguistically expressed uncertainty in milord application to medical diagnosis, AI Communications, 1, 1, pp. 14-31, (1988)	John Wiley and Sons Inc.	English	CSIC - Instituto de Investigación en Inteligencia Artificial (IIIA), Cerdanyola del Valles, Barcelona, Spain	NaN	True	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
8	10.1002/(sici)1098-111x(199911)14:11<1071::aid-int1>3.0.co;2-j	Computational verb systems: verb logic	1999	International Journal of Intelligent Systems	Article	10.0	True	False	Yang, T.	NaN	Brain; Computational methods; Formal logic; Speech processing; Computational verb logic; Human natural languages; Verb logic; Verbs; Artificial intelligence	Computational verb systems are new platforms for artificial intelligence. They embed the dynamical knowledge expressed by dynamical experiences of human brains into machines by using pattern thinking. In this paper, the relationship defined by verbs in human natural languages is modeled by computational verb logic (verb logic for short). To unify different verbs with different contexts into comparable standard forms, the concepts of BE transformation and canonical BE transformation are given. The atomic and molecular verb sentences under canonical BE transformations are also defined for verb logic. The basic verb logic operations are given. Some examples are given to demonstrate the concepts of BE transformation and verb logic.	10.0	Brain and Intelligence in Vertebrates, (1982); Three Pound Universe, (1986); Emperor S New Mind, (1989); English Verb Classes and Alternations A Preliminary Investigation, (1993); Technical Report Memorandum no Ucb Erl M97 66, (1997); Inform Sci, (1998); Internat J Gen Systems, (1998); Elements of English Grammar, (1901); Logic and Boolean Algebra, (1962); Logic and Algorithms, (1966)	John Wiley & Sons Inc	English	University of California, Berkeley, Berkeley, CA, United States	NaN	True	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
9	10.1002/(sici)1099-1743(200003/04)17:2<149::aid-sres290>3.0.co;2-q	Systems research, genetic algorithms and information systems	2000	Systems Research and Behavioral Science	Article	53.0	True	False	Chaudhry, S.S.; Varano, M.W.; Xu, L.	Computational intelligence; Evolution; Genetic algorithms; Information systems; Intelligent systems; Soft computing; Systems science; Systems thinking	NaN	Darwinian evolution and genetics have spawned a class of computational methods called evolutionary algorithms, and in particular, genetic algorithms. These evolutionary strategies provide new opportunities and challenges with ever-increasing applications in industry. In this paper, we propose that the proper context for a basic unifying theory of evolution for the emerging debate on the similarities and differences between biotic evolution and evolutionary algorithms is systems science. Recent changes in technology, coupled with developments in the field of artificial intelligence, promote the growth of enabling technologies, such as intelligent systems, in which we integrate genetic algorithms. Genetic algorithms are integrated with other artificial intelligence tools using a cooperating intelligent subsystem, which is integrated into the information systems of the organization. A portfolio of examples illustrating the evolving and expanding applications of genetic algorithms is included, as well as our computational experience with several commercially available genetic algorithm software. Copyright © 2000 John Wiley & Sons, Ltd.	53.0	Management Science, (1971); AI Expert, (1994); Information and Economic Behavior, (1973); Back, Barbro, Neural networks and genetic algorithms for bankruptcy predictions, Expert Systems with Applications, 11, 4 SPEC. ISS., pp. 407-413, (1996); Knowledge Link how Firms Compete Through Strategic Alliances, (1991); Genetic Programming an Introduction, (1998); American Economic Review, (1966); Stern Information Systems Review, (1993); Chatterjee, Sangit, Genetic algorithms in statistics: Procedures and applications, Communications in Statistics Part B: Simulation and Computation, 26, 4, pp. 1617-1630, (1997); Cors Informs National Meeting at Montreal, (1998)	John Wiley and Sons Ltd	English	Department of Decision and Information Technologies, Villanova University, Villanova, PA, United States; Department of Computer Science and Technology, China University of Mining and Technology, Xuzhou, Jiangsu, China; Department of Decision and Information Technologies, Villanova University, Villanova, PA, United States	NaN	True	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN

	doi	title	year	journal	document_type	cited_by	has_scopus	has_wos	authors	author_keywords	index_keywords	abstract	cited_by_scopus	references_count_scopus	publisher_scopus	language_scopus	affiliations_scopus	country_scopus	source_scopus	title_wos	year_wos	journal_wos	document_type_wos	publication_type_wos	authors_wos	author_full_names_wos	author_keywords_wos	keywords_plus_wos	abstract_wos	times_cited_wos_core	times_cited_wos_all	cited_reference_count_wos	wos_categories	research_areas	publisher_wos	language_wos	affiliations_wos	addresses_wos	source_wos
10821	10.7659/j.issn.1005-6947.2021.02.006	Analysis of key protein regulatory genes in differential expression profile of gallbladder cancer based on bioinformatics approaches; 基于生物信息学的胆囊癌差异表达谱中关键蛋白调控基因分析	2021	China Journal of General Surgery	Article	3.0	True	False	Han, W.; Xin, W.; Su, S.; Wang, Q.	Computational Biology; Gallbladder Neoplasms; Gene Expression Profiling; Protein Interaction Maps	NaN	Background and Aims: The underlying mechanism for the occurrence of gallbladder carcinoma (GBC) is still unclear at present. The available data at the genomic and transcriptomic levels provide the basic data source for investigation of the molecular biological mechanisms of GBC. Therefore, this study was conducted to to analyze the differentially expressed genes in and normal gallbladder tissues and key protein regulatory molecules in GBC by bioinformatics approaches, so as to explore the potential molecular biological mechanism of GBC. Methods: The differentially expressed genes were screened based on two GBC transcriptional datasets from GEO database, and the three GO functional annotations were performed on these genes. The STRING database was applied to construct a protein interaction network, and perform module mining in the investigation of key protein regulatory genes. Finally, the expression and predictive efficacy of the identified key protein regulatory genes were comprehensively evaluated. Results: A total of 140 repeatable differentially expressed genes (20 up-regulated genes and 120 down-regulated genes) in GBC were screened, which are mainly related to the forebrain development and positive regulation of neurogenesis, and participate in the composition of the postsynaptic membrane and transverse tubules. Meanwhile, the SFRP1, a key protein regulatory molecule, had a certain ability in predicting the occurrence of GBC. Conclusion: The information expressed by transcription spectrum of GBC obtained in this study can provide framework and thinking structure for studying the molecular mechanism of GBC. The key protein regulatory molecule SFRP1 probably plays a pivotal role in the occurrence and development of GBC. © Chinese Journal of General Surgery 2021.	3.0	Miller, Kimberly D., Cancer treatment and survivorship statistics, 2016, Ca-A Cancer Journal for Clinicians, 66, 4, pp. 271-289, (2016); Hundal, Rajveer, Gallbladder cancer: Epidemiology and outcome, Clinical Epidemiology, 6, 1, pp. 99-109, (2014); Siegel, Rebecca L., Cancer statistics for Hispanics/Latinos, 2015, Ca-A Cancer Journal for Clinicians, 65, 6, pp. 457-480, (2015); Misra, S., Carcinoma of the gallbladder, The Lancet Oncology, 4, 3, pp. 167-176, (2003); Samuel, Sandeep, Clinicopathological characteristics and outcomes of rare histologic subtypes of gallbladder cancer over two decades: A population-based study, PLOS ONE, 13, 6, (2018); Roa, Juan C., Squamous cell and adenosquamous carcinomas of the gallbladder: Clinicopathological analysis of 34 cases identified in 606 carcinomas, Modern Pathology, 24, 8, pp. 1069-1078, (2011); Reid, Kaye M., Diagnosis and surgical management of gallbladder cancer: A review, Journal of Gastrointestinal Surgery, 11, 5, pp. 671-681, (2007); Henley, S. Jane, Gallbladder cancer incidence and mortality, United States 1999-2011, Cancer Epidemiology Biomarkers and Prevention, 24, 9, pp. 1319-1326, (2015); Kakaei, Farzad, Surgical treatment of gallbladder carcinoma: a critical review, Updates in Surgery, 67, 4, pp. 339-351, (2015); Lazcano-Ponce, Eduardo César, Epidemiology and molecular pathology of gallbladder cancer, Ca-A Cancer Journal for Clinicians, 51, 6, pp. 349-364, (2001)	Central South University	Chinese	Department of General Surgery, Air Force Medical University, Xi'an, Shaanxi, China	NaN	True	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
10822	10.7764/disena.21.article.7	Generative Allegories of Oppression and Emancipation: Reflecting with Computational Social Models; Alegorías generativas de opresión y emancipación: Reflexionando con modelos sociales computacionales	2022	Disena	Article	0.0	True	False	Sosa, R.	Change agents; Creative research methods; Emergence; Multi-agent simulations; Thought experiments	NaN	AThis paper presents a computational approach to growing artificial societies (agent-based simulations) as an explicit, accessible, and systematic tool to visualize and generate insights and new questions about Paulo Freire’s concepts of oppression and emancipation. These models do not make claims of validity or prediction, instead, their value is to structure our thinking and support our understanding. Here, I use computational social simulationas generative allegories to reflect upon the role of designers in participatory, co-design, and social design contexts. The paper shows how Freirean ideas can help reframe design as a pedagogical craft based on dialogue and collective inquiry. © 2022, Pontificia Universidad Catolica de Chile. All rights reserved.	0.0	Design Issues, (2004); Axelrod, Robert M., The dissemination of culture: A model with local convergence and global polarization, Journal of Conflict Resolution, 41, 2, pp. 203-226, (1997); Bertolotti, Francesco, Sensitivity to Initial Conditions in Agent-Based Models, Lecture Notes in Computer Science, 12520 LNAI, pp. 501-508, (2020); Social Agents Ecology Exchange and Evolution, (2002); On Anarchism, (2013); Costopoulos, André, How did sugarscape become a whole society model?, 7, pp. 259-269, (2015); Intuition Pumps and Other Tools for Thinking, (2013); Epstein, Joshua M., Agent-based computational models and generative social science, Complexity, 4, 5, pp. 41-60, (1999); Designs for the Pluriverse Radical Interdependence Autonomy and the Making of Worlds, (2018); Pedagogy of the Oppressed, (1970)	Pontificia Universidad Catolica de Chile	English	Auckland University of Technology, Auckland, AUK, New Zealand; Design & Architecture, Monash University, Melbourne, VIC, Australia	NaN	True	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
10823	10.7765/9781526142290	International law in Europe, 700–1200	2022	NaN	Book	8.0	True	False	Benham, J.	arbitration; expulsion; International law; legal justification; redress; treaties	Computational methods; Information systems; Information use; Kyoto Protocol; Law enforcement; Arbitration; Expulsion; Legal justification; Legal rules; Middle ages; Redress; International law	"It is the contention of this book that there was a notion of international law in the medieval period, and more specifically in the period 700 to 1200. It examines and analyses the ways and the extent to which such as system of rules was known and followed in the Middle Ages by exploring treaties as the main source of international law, and by following a known framework of evidencing it: that it was practised on a daily basis; that there was a reliance upon justification of action; that the majority of international legal rules were consistently obeyed; and finally, that it had the function to resolve disputed questions of fact and law. This monograph further considers problems such as enforcement, deterrence, authority, and jurisdiction, considering carefully how they can be observed in the medieval evidence, and challenging traditional ideas over their role and function in the history of international law. This monograph then, attempts to make a leap forward in thinking about how rulers, communities, and political entities conducted diplomacy and regulated their interactions with each other in a period before fully fledged nation states. © Jenny Benham 2022.	8.0	undefined; undefined; undefined; undefined; undefined; Actes Concernant Les Vicomtes De Marseille Et Leurs Descendants, (1926); Acts of Welsh Rulers 1120 1283, (2005); undefined, (1991); undefined, (1969); Alcuin of York His Life and Letters, (1974)	Manchester University Press	English	NaN	NaN	True	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
10824	10.7771/1541-5015.1754	Tinkering with logo in an elementary mathematics methods course	2018	Interdisciplinary Journal of Problem-based Learning	Article	4.0	True	False	Valentine, K.D.	Computational literacy; Geometry; Mathematics education; Teacher education	NaN	With an increased push to integrate coding and computational literacy in K–12 learning environments, teacher educators will need to consider ways they might support preservice teachers (PSTs). This paper details a tinkering approach used to engage PSTs in thinking computationally as they worked with geometric concepts they will be expected to teach in K–5. Experiences programming in Logo to construct authentic artifacts in the form of two-dimensional geometric graphics not only supported PSTs’ understanding of core geometric and spatial concepts, but also helped them to make connections between mathematics and computational literacy. Artifacts and discourse are discussed as they relate to three core considerations: engaging learners to construct authentic artifacts, supporting a communitarian ethos, and supporting various types of rapid feedback. © 2018, Purdue University Press. All rights reserved.	4.0	Call for Manuscripts Special Issue Tinkering in Technology Rich Design Contexts, (2017); Barr, Valerie B., Bringing computational thinking to K-12: What is involved and what is the role of the computer science education community?, ACM Inroads, 2, 1, pp. 48-54, (2011); Berland, Matthew W., Making, tinkering, and computational literacy, pp. 196-205, (2016); Berland, Matthew W., Using Learning Analytics to Understand the Learning Pathways of Novice Programmers, Journal of the Learning Sciences, 22, 4, pp. 564-599, (2013); Creative Computing, (2014); Proceedings of the 2012 Annual Meeting of the American Educational Research Association, (2012); Common Core State Standards for Mathematics, (2010); Focus in High School Mathematics Technology to Support Reasoning and Sense Making, (2011); Changing Minds Computers Learning and Literacy, (2000); Grover, Shuchi, Computational Thinking in K-12: A Review of the State of the Field, Educational Researcher, 42, 1, pp. 38-43, (2013)	Purdue University Press	English	West Virginia University, Morgantown, WV, United States	NaN	True	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
10825	10.7771/2157-9288.1296	Making makers: Tracing stem identity in rural communities	2021	Journal of Pre-College Engineering Education Research	Article	10.0	True	False	Nixon, J.; Stoiber, A.; Halverson, E.	Computational thinking; Engineering; Identity; Informal education; Makerspaces; Rural education; STEM	NaN	In this article, we describe efforts to reduce barriers of entry to pre-college engineering in a rural community by training local teens to become maker-mentors and staff a mobile makerspace in their community. We bring a communities of practice frame to our inquiry, focusing on inbound and peripheral learning and identity trajectories as a mechanism for representing the maker-mentor experience. Through a longitudinal case study, we traced the individual trajectories of five maker-mentors over two years. We found a collection of interrelated factors present in those students who maintained inbound trajectories and those who remained on the periphery. Our research suggests that the maker-mentors who facilitated events in the community, taught younger community members about making, and co-facilitated with other maker-mentors were more likely to have inbound trajectories. We offer lessons learned from including a mentorship component in a pre-college maker program, an unusual design feature that afforded more opportunities to create inbound trajectories. A key affordance of the maker-mentor program was that it allowed teens to explore areas of making that were in line with their interests while still being a part of a larger community of practice. Understanding learning and identity trajectories will allow us to continually improve pre-college engineering programming and education opportunities that build on students’ funds of knowledge. © 2021, Purdue University Press. All rights reserved.	10.0	Maker Centered Learning and the Development of Self Preliminary Findings of the Agency by Design Project, (2015); Missing Makers how to Rebuild Americas Manufacturing Workforce; Crec Works Newsletter, (2008); Journal of Research in Rural Education, (2011); Barajas-López, Filiberto, Indigenous Making and Sharing: Claywork in an Indigenous STEAM Program, Equity and Excellence in Education, 51, 1, pp. 7-20, (2018); Makeology Makers as Learners, (2016); Barron, Brigid J.S., Predictors of creative computing participation and profiles of experience in two Silicon Valley middle schools, Computers and Education, 54, 1, pp. 178-189, (2010); Makeology Makers as Learners, (2016); Social Justice Education for Teachers Paulo Freire and the Possible Dream, (2008); Blikstein, Paulo, Children are not hackers: Building a culture of powerful ideas, deep learning, and equity in the maker movement, pp. 64-79, (2016)	Purdue University Press	English	PBS Wisconsin Education, United States; University of Wisconsin-Madison, Madison, WI, United States	NaN	True	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
10826	10.7821/naer.2021.7.640	Investigating the Computational Thinking Ability of Young School Students Across Grade Levels in Two Different Types of Romanian Educational Institutions	2021	Journal of New Approaches in Educational Research	Article	7.0	True	True	Kátai, Z.; Osztián, E.; Lörincz, B.	ALGORITHMS; COMPUTER-ASSISTED INSTRUCTION; CURRICULUM; EDUCATIONAL TESTING; GENDER ROLES	NaN	Over the last decade, continuous efforts have been made to bring computational thinking (CT) closer to K-12 education. These focused endeavors implicitly suggest that the current curricula do not sufficiently contribute to the development of learners’ CT. On the other hand, since CT is a combined skill with cross-disciplinary implications, one might conclude that even without an explicit focus on CS education, students’ CT might develop latently as they advance with the current curriculum. We have proposed to test whether differences exist in how 3rd-, 5th-, 7th- and 9th-grade learners from two Romanian educational institutions (girls vs. boys from Art vs. Theoretical school; 214 subjects with no prior experience in CT) relate to learning tasks that require a certain level of CT. The testing tool was inspired by the AlgoRythmics dance choreography illustration of the linear search algorithm and has the potential to reveal different levels of abstracting. Findings emphasize the need for a purposeful and coordinated CS infusion into K-9 education in order to accelerate students’ CT development. © 2021. The Author(s).	7.0	Ackerman, Phillip L., The locus of adult intelligence: Knowledge, abilities, and nonability traits, Psychology and Aging, 14, 2, pp. 314-330, (1999); Ahadi, Alireza, Performance and consistency in learning to program, ACM International Conference Proceeding Series, pp. 11-16, (2017); Aho, Alfred V., Computation and computational thinking, Computer Journal, 55, 7, pp. 833-835, (2012); Atmatzidou, Soumela, Advancing students' computational thinking skills through educational robotics: A study on age and gender relevant differences, Robotics and Autonomous Systems, 75, pp. 661-670, (2016); Brackmann, Christian Puhlmann, Development of computational thinking skills through unplugged activities in primary school, ACM International Conference Proceeding Series, pp. 65-72, (2017); Brown, Neil C.C., Restart: The resurgence of computer science in UK schools, ACM Transactions on Computing Education, 14, 2, (2014); Byrne, Pat, The effect of student attributes on success in programming, pp. 49-52, (2001); Csta K 12 Computer Science Standards, (2017); del Olmo-Muñoz, Javier, Computational thinking through unplugged activities in early years of Primary Education, Computers and Education, 150, (2020); Denning, Peter J., The profession of IT: Beyond computational thinking, Communications of the ACM, 52, 6, pp. 28-30, (2009)	Universidad de Alicante	English	Department of Mathematics and Computer Science, Sapientia Erdélyi Magyar Tudományegyetem, Cluj Napoca, Cluj, Romania	NaN	True	Investigating the Computational Thinking Ability of Young School Students Across Grade Levels in Two Different Types of Romanian Educational Institutions	2021.0	JOURNAL OF NEW APPROACHES IN EDUCATIONAL RESEARCH	Article	J	Katai, Z; Osztian, E; Lorincz, B	Katai, Zoltan; Osztian, Erika; Lorincz, Beata	COMPUTER-ASSISTED INSTRUCTION; ALGORITHMS; CURRICULUM; EDUCATIONAL TESTING; GENDER ROLES	GENDER	Over the last decade, continuous efforts have been made to bring computational thinking (CT) closer to K-12 education. These focused endeavors implicitly suggest that the current curricula do not sufficiently contribute to the development of learners' CT. On the other hand, since CT is a combined skill with cross-disciplinary implications, one might conclude that even without an explicit focus on CS education, students' CT might develop latently as they advance with the current curriculum. We have proposed to test whether differences exist in how 3rd-, 5th-, 7th- and 9th-grade learners from two Romanian educational institutions (girls vs. boys from Art vs. Theoretical school; 214 subjects with no prior experience in CT) relate to learning tasks that require a certain level of CT. The testing tool was inspired by the AlgoRythmics dance choreography illustration of the linear search algorithm and has the potential to reveal different levels of abstracting. Findings emphasize the need for a purposeful and coordinated CS infusion into K-9 education in order to accelerate students' CT development.	9.0	10.0	40.0	Education & Educational Research	Education & Educational Research	UNIV ALICANTE, GRUPO INVESTIGACION EDUTIC-ADEI	English	Sapientia Hungarian University of Transylvania	[Katai, Zoltan; Osztian, Erika; Lorincz, Beata] Sapientia Hungarian Univ Transylvania, Dept Math & Informat, Cluj Napoca, Romania	True
10827	10.9779/pauefd.1438401	An Overview of the Epistemological Link between Mathematical Thinking and Computational Thinking from Theory to Practice	2025	PAMUKKALE UNIVERSITESI EGITIM FAKULTESI DERGISI-PAMUKKALE UNIVERSITY JOURNAL OF EDUCATION	Article	1.0	False	True	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	An Overview of the Epistemological Link between Mathematical Thinking and Computational Thinking from Theory to Practice	2025.0	PAMUKKALE UNIVERSITESI EGITIM FAKULTESI DERGISI-PAMUKKALE UNIVERSITY JOURNAL OF EDUCATION	Article	J	Aksoy, BD; Cantürk Günhan, B; Mumcu, F	Aksoy, Behiye Dincer; Canturk Gunhan, Berna; Mumcu, Filiz	Mathematical thinking; Computational thinking; Mathematical modeling	ABSTRACTION	Mathematical thinking is critical to the maintenance of daily life and the development of science. This study examines the epistemological connection between mathematical and Computational Thinking (CT). Since CT involves the problem-solving process through thinking and computer science tools, it is thought to have an important relationship with mathematical thinking. In order to understand this relationship, mathematical modeling problems are addressed from a cognitive perspective in this study. Using a case study, a conceptual framework was developed by examining studies that attempt to integrate CT into mathematics education. In line with the framework, a professional development course was prepared and administered to a mathematics teacher. Subsequently, the relationship between the cognitive processes in the modeling process and the components of CT was examined. The study's findings revealed that (a) considering abstraction in the context of Piaget's abstraction theory is a more effective approach for understanding mathematical thinking, (b) more than one CT component can be identified at each stage of the modeling process, and no single component can be exclusively associated with a single stage, and (c) common and distinct thinking processes between mathematical thinking and CT were uncovered. These findings contribute to a more nuanced comprehension of the intricate interrelationships between mathematical thinking and cognitive flexibility.	1.0	1.0	67.0	Education & Educational Research	Education & Educational Research	DERGIPARK AKAD	English	Dokuz Eylul University; Dokuz Eylul University; Celal Bayar University	[Aksoy, Behiye Dincer] Dokuz Eylul Univ, Egitim Bilimleri Enstitusu, Izmir, Turkiye; [Canturk Gunhan, Berna] Dokuz Eylul Univ, Izmir, Turkiye; [Mumcu, Filiz] Celal Bayar Univ, Yunusemre, Turkiye	True
10828	10.9779/pauefd.696511	The Relationship between Mathematics Teachers' Mind Types and Computational Thinking Skills	2021	PAMUKKALE UNIVERSITESI EGITIM FAKULTESI DERGISI-PAMUKKALE UNIVERSITY JOURNAL OF EDUCATION	Article	3.0	False	True	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	The Relationship between Mathematics Teachers' Mind Types and Computational Thinking Skills	2021.0	PAMUKKALE UNIVERSITESI EGITIM FAKULTESI DERGISI-PAMUKKALE UNIVERSITY JOURNAL OF EDUCATION	Article	J	Hidiroglu, YÖ; Hidiroglu, ÇN	Hidiroglu, Yeliz Ozkan; Hidiroglu, Caglar Naci	Computational thinking; the five minds for future; mathematics teacher	FRAMEWORK; VALIDITY	The aim of the study is to investigate the relationship between the mind types of mathematics teachers which will shape the future and their computational thinking skills. The study was designed according to quantitative-relational survey model. This study was carried out with 481 volunteer mathematics teachers determined according to the random sampling method. Computational Thinking Skills Scale and Mind Types Scale were used as data collection tools in the study. In the analysis of the data, descriptive statistics, correlation and regression analyses were benefited. According to the perceptions of the mathematics teachers, the level of their ethical mind and computational thinking skills are very high while their disciplined mind, synthesizing mind, creating mind, respectful mind and quinary mind levels are high. Also, according to the perceptions of mathematics teachers, there is a high level significant positive relationship between their quinary minds and computational thinking skills, and their quinary minds (both in sub-dimesnions and as a whole) are a significant predictor of their computational thinking.	1.0	3.0	46.0	Education & Educational Research	Education & Educational Research	DERGIPARK AKAD	English	Ministry of National Education - Turkey; Pamukkale University	[Hidiroglu, Yeliz Ozkan] Republ Turkey Minist Natl Educ, Ankara, Turkey; [Hidiroglu, Caglar Naci] Pamukkale Univ, Math Educ, Denizli, Turkey	True
10829	10.9781/ijimai.2021.03.001	Foundations for the design of a creative system based on the analysis of the main techniques that stimulate human creativity	2021	International Journal of Interactive Multimedia and Artificial Intelligence	Article	2.0	True	False	de Garrido, L.; Gómez-Sanz, J.J.; Pavón, J.	Artificial Intelligence Creative System; Creativity; Lateral Thinking; Methods to Stimulate Human Creativity; Multi-agent System	NaN	This work presents the design of a computational system with creative capacity, based on the synthesis of the main methods that stimulate human creativity. When analyzing each method, a set of characteristics that the computer system must have in order to emulate a creative capacity has been suggested. In this way, by integrating all the suggestions in a structured way, it is possible to design the general architecture and functioning strategy of a computer system that has the incremental creative capacity of well-known creative methods. This computational system is designed as a multi-agent system, made up of two groups of agents, the problem solving group and the creative group, the first one exploring and evaluating paths for suitable solutions, the second implementing creative methods to generate new paths that are provided to the first group. © 2021, Universidad Internacional de la Rioja. All rights reserved.	2.0	de Garrido, Luis, Agent-based modeling of collaborative creative processes with INGENIAS, AI Communications, 32, 3, pp. 223-233, (2019); Boden, Margaret A., Creativity and artificial intelligence, Artificial Intelligence, 103, 1-2, pp. 347-356, (1998); Artificial Intelligence, (1996); Jennings, Kyle E., Developing creativity: Artificial barriers in artificial intelligence, Minds and Machines, 20, 4, pp. 489-501, (2010); Intelligence Without Reason, (1991); Phi Delta Kappan, (1961); Jordanous, Anna, Four PPPPerspectives on computational creativity in theory and in practice, Connection Science, 28, 2, pp. 194-216, (2016); Lamb, Carolyn Elizabeth, Evaluating computational creativity: An interdisciplinary tutorial, ACM Computing Surveys, 51, 2, (2019); Wiggins, Geraint A., A preliminary framework for description, analysis and comparison of creative systems, Knowledge-Based Systems, 19, 7, pp. 449-458, (2006); Role of Creativity in the Management of Innovation, (2017)	Universidad Internacional de la Rioja	English	Department of Architecture, Universitat de València, Valencia, Valencia, Spain; Institute of Knowledge Technology, Universidad Complutense de Madrid, Madrid, Madrid, Spain	NaN	True	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
10830	NaN	Cultivating Computational Thinking Skills via Educational Robotics Activities in a Blended Learning Environment	2025	CEUR Workshop Proceedings	Conference paper	0.0	True	True	Pappa, N.	Blended Learning; Computational Thinking; Educational Robotics; Robotics Simulators; Secondary Education1	Adversarial machine learning; Educational robots; Federated learning; Robot learning; Blended learning; Blended learning environments; Computational thinkings; Educational levels; Educational robotics; Learning context; Learning outcome; Robotic simulator; Secondary education1; Thinking skills; Contrastive Learning	There is a significant trend in the integration of Educational Robotics at all educational levels, and along with this, the promotion of Computational Thinking is one of the related learning outcomes of this integration. A t the s ame t ime, the t ransfer o f f ace-to-face learning to online or Blended Learning context due to the COVID-19 pandemic has led to the development of several technological tools, such as Educational Robotic simulators and online collaborating environments, to support this transfer. In this field, this PhD research aims to design a framework in which students collaborate in a Blended Learning context while solving Educational Robotic activities to cultivate Computational Thinking skills. © 2025 Copyright for this paper by its authors.	0.0	Wing, Jeannette M., Computational thinking, Communications of the ACM, 49, 3, pp. 33-35, (2006); Developing Computational Thinking in Compulsory Education, (2016); Atmatzidou, Soumela, How Does the Degree of Guidance Support Students’ Metacognitive and Problem Solving Skills in Educational Robotics?, Journal of Science Education and Technology, 27, 1, pp. 70-85, (2018); Chiazzese, Giuseppe, Educational robotics in primary school: Measuring the development of computational thinking skills with the bebras tasks, Informatics, 6, 4, (2019); Chevalier, Morgane, Fostering computational thinking through educational robotics: a model for creative computational problem solving, International Journal of STEM Education, 7, 1, (2020); Ioannou, Andri, Exploring the potentials of educational robotics in the development of computational thinking: A summary of current research and practical proposal for future work, Education and Information Technologies, 23, 6, pp. 2531-2544, (2018); Alves Gomes, Andresa Shirley, Educational Robotics in Times of Pandemic: Challenges and Possibilities, (2020); Proceedings of the 2012 Annual Meeting of the American Educational Research Association, (2012); Román-González, Marcos, Combining Assessment Tools for a Comprehensive Evaluation of Computational Thinking Interventions, pp. 79-98, (2019); Chevalier, Morgane, The role of feedback and guidance as intervention methods to foster computational thinking in educational robotics learning activities for primary school, Computers and Education, 180, (2022)	CEUR-WS	English	University of West Attica, Athens, Attica, Greece	NaN	True	Exploring the Differences in the Cultivation of Computational Thinking in Primary through Meta-analysis based on the Perspective of the Contrast between the East and the West	2021.0	29TH INTERNATIONAL CONFERENCE ON COMPUTERS IN EDUCATION (ICCE 2021), VOL II	Proceedings Paper	C	Guan, X; Wei, GX; Jiang, B; Feng, X	Guan, Xiu; Wei, Guoxia; Jiang, Bo; Feng, Xiang	Computational thinking; comparative analysis; meta-analysis	LEARNING-PERFORMANCE; GAME	With the advent of the era of intelligent education, the cultivation and development of computational thinking is the key in talent training. However, most of the existing researches focus on the design of computational thinking teaching methods and models on a small scale, and lack the test of the training effect. Moreover, these effects in existing research are also mixed and fuzzed, and there are even greater differences between the East and the West. Therefore, in order to be able to analyze the effects of computational thinking teaching in depth, meta-analysis can be used to extract the factors that influence the effects of computational thinking in the related research on computational thinking training in the primary school stage in the East and the West. Through the calculation of experimental effect size, the effects of different studies are merged, so as to present the true effect of computational thinking training. A total of 30 qualified literatures were filtered, and 278 effect values were extracted from them. Based on these, the difference in training effects between the East and the West can be calculated to further analyze the development differences of computational thinking in different regions and teaching methods, and then point out the direction for the improvement of computational thinking training methods and models between different regions. The main value of the research is promoting the innovative development of computational thinking training within the globe.	1.0	1.0	34.0	Computer Science, Interdisciplinary Applications; Education & Educational Research	Computer Science; Education & Educational Research	ASIA PACIFIC SOC COMPUTERS IN EDUCATION	English	East China Normal University; Zhejiang University of Technology	[Guan, Xiu; Jiang, Bo; Feng, Xiang] East China Normal Univ, Dept Educ Informat Technol, Shanghai, Peoples R China; [Wei, Guoxia] Zhejiang Univ Technol, Coll Educ, Hangzhou, Zhejiang, Peoples R China	True

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